WO2013170643A1 - 社交网络节点分组方法和装置、计算机存储介质 - Google Patents

社交网络节点分组方法和装置、计算机存储介质 Download PDF

Info

Publication number
WO2013170643A1
WO2013170643A1 PCT/CN2013/071038 CN2013071038W WO2013170643A1 WO 2013170643 A1 WO2013170643 A1 WO 2013170643A1 CN 2013071038 W CN2013071038 W CN 2013071038W WO 2013170643 A1 WO2013170643 A1 WO 2013170643A1
Authority
WO
WIPO (PCT)
Prior art keywords
node
nodes
group
candidate
correlation
Prior art date
Application number
PCT/CN2013/071038
Other languages
English (en)
French (fr)
Inventor
刘跃文
陈川
贺鹏
麦君明
李玉煌
陈伟华
Original Assignee
腾讯科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 腾讯科技（深圳）有限公司 filed Critical 腾讯科技（深圳）有限公司
Priority to JP2015511906A priority Critical patent/JP5946956B2/ja
Priority to US14/401,271 priority patent/US10169476B2/en
Priority to KR1020147035105A priority patent/KR101678115B1/ko
Publication of WO2013170643A1 publication Critical patent/WO2013170643A1/zh
Priority to US16/197,231 priority patent/US11361045B2/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

Definitions

  • the present invention relates to Internet technologies, and in particular, to a social network node grouping method and apparatus, and a computer storage medium.
  • social networks are all websites and products that provide connections between people, including but not limited to instant messaging products, social networking sites, chat rooms, BBS, virtual communities, online games. Wait.
  • a user can act as a node, and there is a direct friend relationship or an indirect relationship between the users, that is, there is an association relationship between the nodes or there may be an association relationship between the nodes.
  • the social network provides a possible relationship between the nodes, which can promote the development of the relationship chain between the nodes, so that the related relationship may be transformed into the existing relationship.
  • a certain user is used as a target node, and a node having an association relationship with the target node is used as an association node, and a node having a potential association relationship with the target node is used as a candidate node.
  • the associated node is a user friend
  • the candidate node is a potential friend.
  • the potential friend refers to a user who may become a user friend, so that the user can be provided with a potential friend recommendation to promote the user to develop its online relationship chain.
  • association node and the candidate node are generally divided into two large blocks, which are respectively displayed to the target node.
  • the target node can only interact with the associated node with which it has an associated relationship. If it is to interact with a candidate node that has a potential association relationship, the candidate node needs to be changed to an associated node.
  • the target node needs to obtain the data of the candidate node, it needs to search for the block where the candidate node is located, which is inconvenient to operate, and is not convenient for the target node to rapidly expand its relationship chain.
  • a social network node grouping method includes the following steps:
  • Each packet association node and candidate node are combined and output according to the correlation.
  • a social network node grouping device includes:
  • An extraction module configured to acquire a candidate node that has a potential association relationship with the target node, an associated node that has an association relationship with the target node, and a group identifier of the associated node;
  • a processing module configured to acquire, by the group identifier, a correlation degree between each group association node and the candidate node and the target node;
  • an output module configured to perform combined output on each group association node and candidate node according to the correlation.
  • One or more computer storage media containing computer executable instructions for performing a social network node grouping method, the method comprising the steps of:
  • the associated nodes and candidate nodes in each group are combined and output according to the correlation.
  • the social network node grouping method and device, and the computer storage medium acquire the group identifiers of the candidate nodes, the associated nodes, and the associated nodes, acquire the correlation degree between the candidate nodes and the associated nodes in each group identifier, and group the groups according to the correlation degree.
  • the intra-association node and the candidate node are combined and outputted, which is convenient for operation, reduces the operation of adding a candidate node by the user, and improves the system response efficiency.
  • 1 is a schematic flow chart of a method for grouping social network nodes in an embodiment
  • step S20 is a schematic diagram of a specific process of step S20 in an embodiment
  • FIG. 3 is a schematic diagram of a node relationship in an embodiment
  • FIG. 4 is a schematic diagram showing an associated node and a candidate node in an embodiment
  • FIG. 5 is a schematic diagram showing friends and potential friends in an embodiment
  • FIG. 6 is a schematic structural diagram of a social network node grouping apparatus in an embodiment
  • FIG. 7 is a schematic diagram showing the internal structure of a processing module in an embodiment
  • FIG. 8 is a schematic diagram showing the internal structure of an output module in an embodiment
  • FIG. 9 is a schematic structural diagram of a social network node grouping apparatus in another embodiment.
  • a social network node grouping method includes the following steps:
  • Step S10 Obtain a candidate node that has a potential association relationship with the target node, an associated node that has an association relationship with the target node, and a group identifier of the associated node.
  • a certain user is selected as a target node in the social network, and a user having a friend relationship with the user, that is, a node having an association relationship with the target node is an associated node; and a user who may have a friend relationship with the user, that is, a potential node with the target node
  • the nodes of the association are candidate nodes.
  • the potential association relationship means that the candidate node will possibly be associated with the target node.
  • the candidate node having the potential association relationship of the target node may be obtained in advance, and the candidate node is placed in the candidate node list, and then the candidate node corresponding to the target node is obtained from the candidate node list. There are many ways to obtain candidate nodes in the candidate node list.
  • the matching weight of the attribute information of the target node is preset, and then the attribute information of the node that has no association relationship with the target node is compared with the attribute information of the target node, and the right of the node that has no association relationship with the target node is obtained.
  • a value, a node having a weight greater than a threshold that is not associated with the target node is used as a candidate node.
  • the attribute information of the node may include real information such as gender, age, constellation, blood type, graduation school, major, graduation time, place of origin, location, industry, hobbies, etc.; if it is a virtual social network, it may also include a virtual world. The area in which it is located, the avatar attribute, the avatar level, and so on.
  • the group identification may include junior high school students, high school students, university students, colleagues, family members, etc. but is not limited thereto.
  • the packet identifier may also be a packet ID number, such as 001 packet, 002 packet.
  • Step S20 Acquire an association between the associated node in each group identifier and the candidate node and the target node.
  • the correlation between the candidate node and the target node and the correlation between the associated node and the target node in each group identifier are respectively obtained according to the group identifier.
  • Correlation is the degree to which a candidate node or associated node matches the attribute information of the target node.
  • the degree of relevance may be the degree of similarity of the attribute information between the user and the user.
  • step S20 specifically includes:
  • Step S210 Predetermine a condition that has an association relationship with a node in the group.
  • the target node, the associated node, and the candidate node are all nodes.
  • condition that the preset relationship with the node in the group is preset may include at least one of the following:
  • the node itself is a node within the group.
  • the associated node B itself counts one.
  • the node has an association with the nodes in the group.
  • the associated node B if there is an associated node B, an associated node C, and an associated node D in the 001 packet of the target node A, and the associated node B has an association relationship with the associated node C, and the associated node B serves as a node within the 001 packet in which the node statistics are associated.
  • the associated node C meets the condition as an intra-001 packet in the associated node B that has an associated relationship.
  • the node has a relationship with a preset number of candidate nodes, and a predetermined number of candidate nodes have an association relationship with a group of nodes, and the node has an association relationship with the node in the group.
  • the associated node in the group is the friend of the user, and the candidate node is a friend who may become the user.
  • the 001 packet of the target node A has the associated node B, the associated node C, and the associated node D.
  • the candidate nodes outside the packet are E, F, G, and H.
  • the preset number is three, if E and F, G, and H exist.
  • Correlation relationship, B has an association relationship with F, G, and H.
  • Step S220 Obtain the number of nodes in the group in which each node has an association relationship according to conditions.
  • the number of nodes in the 001 packet in which the association relationship of the associated node B exists is counted according to the condition, and the number of nodes in the 002 packet in which the associated relationship of the associated node B exists is counted.
  • Step S230 the ratio of the number of nodes in the group of the existence relationship of each node to the number of nodes in the group is used as the correlation degree between each node and the target node.
  • each node and the target node there is a group G1 associated with the target node A, wherein there are seven nodes associated with the node B1 to B7, and there are candidates. Nodes A1 through A4 have a total of four nodes.
  • the preset number is three, and the nodes in the group include only the associated nodes for processing, and the nodes in the G1 group that are associated with A1 include B1 (conditions (2)), B3 (conditions (2)), and B6 ( Eligible for conditions (2)(3)) and B5 (conforming to condition (3)), so the correlation is 4/7; the intra-group nodes associated with B5 include B5 (eligible (1)), B1 (conforming Conditions (2)), B2 (in accordance with condition (2)), B4 (in accordance with condition (2)), B7 (in accordance with condition (2)), and B6 (in accordance with condition (3)), the correlation is 6/7.
  • the in-group node including the associated node it is beneficial to obtain the correlation degree of the candidate node is greater than the correlation degree of the associated node. If the correlation degree is sorted from high to low, more candidate nodes are ranked in front, which is convenient for the candidate. The node performs related operations.
  • the node in the group includes the associated node and the target node, that is, the target node is used as a statistical node for obtaining the correlation.
  • the nodes in the G1 group that are associated with A1 include B1 (conditions (2), B3 (conditions (2)), B6 (conditions (2), (3)), and B5 (conditions (3)). Therefore, the correlation is 4/8; the intra-group nodes associated with B5 include B5 (conditions (1)), B1 (conditions (2)), B2 (conditions (2)), and B4 (conforms).
  • Condition (2)), B7 (in accordance with condition (2)), B6 (in accordance with condition (3)), and target node A the correlation is 7/8.
  • Step S30 combining the associated nodes and the candidate nodes in each group identifier according to the correlation degree.
  • the associated nodes and candidate nodes in each group can be arbitrarily combined and then output.
  • step S30 is specifically: sorting the associated nodes and candidate nodes in each group identifier according to the correlation degree; displaying the sorted associated nodes and candidate nodes according to the group identifier.
  • the associated nodes and candidate nodes in the group are sorted according to the correlation degree.
  • the correlations of the group identifiers G1, A1 to A4 are 4/7, 2/7, 2/7, 2/7, respectively
  • the correlations of B1 to B7 are 5/7, 5/7, respectively.
  • 4/7, 4/7, 6/7, 6/7, 3/7 sorted from high to low according to the degree of relevance to B5, B6, B3, B4, A1, B7, A2, A3, A4.
  • the display interface is provided with: display node, candidate node, all three selection controls, and associated node list.
  • Group 1 includes an associated node 1, a candidate node 1, an associated node 2, a candidate node 2, an associated node 3, a candidate node 3, and a candidate node 4, and the packet 2 includes an associated node 4, an associated node 5, a candidate node 5, and an association.
  • Node 6 when the associated node and the candidate node are displayed, different identifiers may be marked to distinguish, as shown in FIG. 4, the associated node is marked with a solid smile, and the candidate node is marked with a dotted smile. The identifier may be set by the user or set by the system. .
  • the candidate node when the correlation degree of the candidate node in the packet identifier is 0, the candidate node is hidden within the packet identifier or the candidate node is not added to the packet identifier.
  • the 001 packet is processed first, the correlation between the candidate node H and the target node is 0, then the candidate node H does not join the 001 packet, and when the 002 packet is processed, the correlation between the candidate node H and the target node is not 0, then the candidate node H Join the 002 packet.
  • the foregoing social network node grouping method the step of sorting the associated nodes and the candidate nodes in each group identifier according to the correlation degree is specifically: identifying the associated nodes in each group according to the correlation degree from high to low The candidate nodes are sorted.
  • the step of displaying the sorted associated node and the candidate node according to the group identifier is specifically: displaying, in each group identifier, a preset result with a high correlation degree of a preset number or a user selected number.
  • the correlations of the groups G1, A1 to A4 are 4/7, 2/7, 2/7, 2/7, respectively, and the correlations of B1 to B7 are 5/7, 5/7, respectively. 4/7, 4/7, 6/7, 6/7, 3/7, sorted by relevance from high to low, A3, A4, preset number is 7, then show B5, B6, B3 , B4, A1, B7, A2. If the number of users selected is 6, the B5, B6, B3, B4, A1, and B7 are displayed. In addition, the number of user choices can be adjusted by the user at any time.
  • the social network node grouping method after the step of acquiring the correlation degree of each group association node and the candidate node and the target node, further includes the steps of: setting a correlation threshold; and hiding the correlation less than the correlation threshold.
  • the correlation threshold may be set by the user or the system according to requirements. After the correlation threshold is set in advance, the candidate node is determined only. When the correlation degree of the candidate node is less than the correlation threshold, the candidate node is hidden.
  • the candidate node When the ratio is greater than or equal to , the candidate node is displayed.
  • the associated nodes for the target node can all be presented to the user.
  • the user can set the correlation threshold as needed, and the specific implementation can be as follows: setting the slider control on the interface, and sliding the slider to adjust the correlation threshold. After the sorted associated node and the candidate node are displayed, the candidate nodes whose correlation degree is less than the correlation threshold may be hidden according to the relevance threshold set by the user.
  • the foregoing social network node grouping method may further sort the associated nodes and candidate nodes after the candidate node whose relevance is less than the correlation threshold in each group from high to low, and A sort result with a high degree of correlation between the preset number or the number of user selections displayed in each group identifier.
  • the foregoing social network node grouping method further includes the steps of: obtaining a display setting selected by the user; and displaying the sorted associated node and the candidate node according to the display setting and the group identifier.
  • the display setting selected by the user may be that only the associated node is displayed, only the candidate node is displayed, and any one of the displays is displayed, and the corresponding display is performed according to the display setting. If only the associated nodes are displayed, B1 to B7 are displayed, and only the candidate nodes are displayed, then A1 to A4 are displayed, and all are displayed, and all the associated nodes and candidate nodes after sorting are displayed.
  • the specific steps of the social network node grouping method applied to the user friend and the potential friend group of the user include:
  • the potential friend of the user refers to a person that the user may know or a person who may become a friend.
  • the potential friend is the candidate node
  • the friend group is the group identifier
  • the friends in the group are the associated nodes.
  • a list of potential friends can be obtained in advance, and potential friends of the user can be obtained from the list of potential friends.
  • the potential friends in the potential buddy list are obtained in a plurality of ways, for example, matching the personal attribute information of the preset user, and then comparing the personal attribute information of the non-friend user with the personal attribute information of the user to obtain the non-friend user.
  • Weight a non-friend user whose weight is greater than the threshold as a potential friend.
  • the user's personal attribute information may include real information such as gender, age, constellation, blood type, graduation school, major, graduation time, place of origin, location, industry, hobbies, etc.; if it is a virtual social network, it may also include virtual The region in the world, virtual character attributes, avatar levels, etc.
  • the group of friends may include junior high school students, high school students, college students, colleagues, family members, etc. but is not limited thereto.
  • a group of friends such as junior high school classmate B in user A's junior high school class.
  • the preset potential friends and the friends in the group are nodes, and the conditions for the relationship with the nodes in the group are preset.
  • the conditions are as described above, and the conditions that are associated with the nodes in the packet identifier are not described herein.
  • (c) Specifically: (c1) Sort each group of friends and potential friends according to relevance.
  • the display interface has a display: friends, potential friends, all three selection controls, friends.
  • the social network node grouping method is applied to the user friend and the potential friend group of the user, and after step (b), the steps are as follows:
  • the correlation threshold may be set by the user or the system according to requirements. After the relevance threshold is set in advance, the potential friend is only judged. When the relevance of the potential friend is less than the relevance threshold, the potential friend is hidden. , showing the potential friend. All of the user's friends can be shown to the user.
  • the user can set the correlation threshold as needed, and the specific implementation can be as follows: setting the slider control on the interface, and sliding the slider to adjust the correlation threshold.
  • the specific steps of the social network node grouping method applied to the user friend and the potential friend group of the user further include:
  • the display setting selected by the user may be any of displaying only a friend, displaying only a potential friend, and displaying all, and then performing corresponding display according to the display setting. If only the friends are displayed, the friends 1 to 6 are displayed, and only the potential friends are displayed, and the potential friends 1 to 5 are displayed. When all are displayed, all the friends and potential friends after the ranking are displayed.
  • a social network node grouping device includes an extraction module 10, a processing module 20, and an output module 30. among them:
  • the extraction module 10 is configured to acquire a candidate node that has a potential association relationship with the target node, an associated node that has an association relationship with the target node, and a group identifier of the associated node.
  • a certain user is selected as a target node in the social network, and a user having a friend relationship with the user, that is, a node having an association relationship with the target node is an associated node; and a user who may have a friend relationship with the user, that is, a potential node with the target node
  • the nodes of the association are candidate nodes.
  • a candidate node that has a potential association relationship with the target node may be obtained in advance, and the candidate node is placed in the candidate node list, and then the candidate node corresponding to the target node is obtained from the candidate node list. There are many ways to obtain candidate nodes in the candidate node list.
  • the matching weight of the attribute information of the target node is preset, and then the attribute information of the node that has no association relationship with the target node is compared with the attribute information of the target node, and the right of the node that has no association relationship with the target node is obtained.
  • a value, a node having a weight greater than a threshold that is not associated with the target node is used as a candidate node.
  • the attribute information of the node may include real information such as gender, age, constellation, blood type, graduation school, major, graduation time, place of origin, location, industry, hobbies, etc.; if it is a virtual social network, it may also include a virtual world. The area in which it is located, the avatar attribute, the avatar level, and so on.
  • the group identification may include junior high school students, high school students, university students, colleagues, family members, etc. but is not limited thereto.
  • the packet identifier may also be a packet ID number, such as 001 packet, 002 packet.
  • the processing module 20 is configured to acquire the correlation between the associated node in each group identifier and the candidate node and the target node. Specifically, the correlation between the candidate node and the target node and the correlation between the associated node and the target node in each group identifier are respectively obtained according to the group identifier. Correlation is the degree to which a candidate node or associated node matches the attribute information of the target node. In this embodiment, the degree of relevance may be the degree of similarity of the attribute information between the user and the user.
  • the processing module 20 includes an initialization unit 210, a statistics unit 220, and an acquisition unit 230. among them:
  • the initialization unit 210 is configured to preset a condition that has an association relationship with a node in the group.
  • the target node, the associated node, and the candidate node are all nodes.
  • the relationship between the statistical node and the node within each packet identifier.
  • condition that the preset association relationship with the intra-group association may include at least one of the following:
  • the nodes themselves are divided into intra-group nodes.
  • the associated node B is the associated node within the 001 packet, and the associated node B counts the number of nodes in the group in which the association relationship exists, the associated node B itself counts one.
  • the node has an association with the nodes in the group.
  • the associated node B if there is an associated node B, an associated node C, and an associated node D in the 001 packet of the target node A, and the associated node B has an association relationship with the associated node C, and the associated node B serves as a node within the 001 packet in which the node statistics are associated.
  • the associated node C meets the condition as an intra-001 packet in the associated node B that has an associated relationship.
  • the node has a relationship with a preset number of candidate nodes, and a predetermined number of candidate nodes have an association relationship with a group of nodes, and the node has an association relationship with the node in the group.
  • the associated node in the group is the friend of the user, and the candidate node is a friend who may become the user.
  • the 001 packet of the target node A has the associated node B, the associated node C, and the associated node D.
  • the candidate nodes outside the packet are E, F, G, and H.
  • the preset number is three, if E and F, G, and H exist.
  • Correlation relationship, B has an association relationship with F, G, and H.
  • the statistic unit 220 is configured to respectively acquire the number of nodes in the group of the existence association relationship of each node according to the condition. Specifically, the number of nodes in the 001 packet of the existing association relationship of the associated node B is counted according to the condition, and the number of nodes in the 002 group of the existing association relationship of the associated node B is counted.
  • the obtaining unit 230 is configured to use the ratio of the number of nodes in the group of the existence association relationship of each node to the number of nodes in the group as the correlation degree between each node and the target node. Specifically, as shown in FIG. 3, details are not described herein again.
  • the node in the group identifier includes the associated node and the target node, that is, the target node is used as a statistical node for obtaining the correlation.
  • the nodes in the G1 group that are associated with A1 include B1 (conditions (2), B3 (conditions (2)), B6 (conditions (2), (3)), and B5 (conditions (3)). Therefore, the correlation is 4/8; the intra-group nodes associated with B5 include B5 (conditions (1)), B1 (conditions (2)), B2 (conditions (2)), and B4 (conforms).
  • Condition (2)), B7 (in accordance with condition (2)), B6 (in accordance with condition (3)), and target node A the correlation is 7/8.
  • the output module 30 is configured to perform combined output on the associated node and the candidate node in each group identifier according to the correlation.
  • the output module 30 arbitrarily combines the associated nodes and the candidate nodes in each group and outputs them.
  • the output module 30 includes a sorting unit 310 and a display unit 320. among them:
  • the sorting unit 310 is configured to sort the associated nodes and the candidate nodes in each group identifier according to the relevance. Specifically, within each group identifier, the associated nodes and candidate nodes in the group identifier are sorted according to the correlation degree. As shown in FIG. 3, the correlations of the group identifiers G1, A1 to A4 are 4/7, 2/7, 2/7, 2/7, respectively, and the correlations of B1 to B7 are 5/7, 5/7, respectively. 4/7, 4/7, 6/7, 6/7, 3/7, sorted from high to low according to the degree of relevance to B5, B6, B3, B4, A1, B7, A2, A3, A4. In addition, you can sort from low to high according to relevance.
  • the display unit 320 is configured to display the sorted associated nodes and candidate nodes according to the group identifier.
  • Association node list group 1 includes an association node 1, a candidate node 1, an association node 2, a candidate node 2, an association node 3, a candidate node 3, and a candidate node 4, and the packet 2 includes an association node 4, an association node 5, and a candidate node. 5 and associated node 6.
  • identifiers may be marked to distinguish, as shown in FIG. 4, the associated node is marked with a solid smile, and the candidate node is marked with a dotted smile.
  • the identifier may be set by the user or set by the system. .
  • the candidate node when the correlation degree of the candidate node in the packet identifier is 0, the candidate node is hidden within the packet identifier or the candidate node is not added to the packet identifier.
  • the 001 packet is processed first, the correlation between the candidate node H and the target node is 0, then the candidate node H does not join the 001 packet, and when the 002 packet is processed, the correlation between the candidate node H and the target node is not 0, then the candidate node H Join the 002 packet.
  • the sorting unit 310 is further configured to sort the associated nodes and the candidate nodes in each group according to the correlation degree from high to low; the displaying unit 320 is further configured to display a preset number or user in each group identifier. Select the number of highly correlated sort results. Specifically, referring to FIG. 3, the correlations of the group identifiers G1, A1 to A4 are 4/7, 2/7, 2/7, 2/7, respectively, and the correlations of B1 to B7 are 5/7, 5/ respectively. 7, 4/7, 4/7, 6/7, 6/7, 3/7, sorted according to the degree of relevance from high to low, A3, A4, the preset number is 7, then B5, B6, B3, B4, A1, B7, A2. If the number of users selected is 6, the B5, B6, B3, B4, A1, and B7 are displayed.
  • the social network node grouping device includes a preset module 40, a determining module 50, a hiding module 60, and an input module, in addition to the extracting module 10, the processing module 20, and the output module 30. 70. among them:
  • the preset module 40 is used to set the relevance threshold.
  • the relevance threshold may be set by the user or the system as needed.
  • the determining module 50 is configured to determine whether the relevance of the candidate node is less than a relevance threshold.
  • the hiding module 60 is configured to hide the candidate node when the relevance of the candidate node is less than the relevance threshold.
  • the sorting unit 310 is further configured to sort the associated nodes and the candidate nodes after the candidate nodes whose correlation degree is less than the correlation threshold is hidden in each group according to the correlation degree.
  • the display unit 320 is further configured to display the associated nodes and candidate nodes that are hidden by the candidate nodes whose correlation is less than the correlation threshold and sorted according to the relevance.
  • the input module 70 is configured to acquire display settings selected by the user.
  • the display setting selected by the user may be that only the associated node is displayed, only the candidate node is displayed, and any one of the displays is displayed, and the corresponding display is performed according to the display setting. If only the associated nodes are displayed, B1 to B7 are displayed, and only the candidate nodes are displayed, then A1 to A4 are displayed, and all are displayed, and all the associated nodes and candidate nodes after sorting are displayed.
  • the display unit 320 is further configured to display the sorted associated nodes and candidate nodes according to the display settings and the group identifier.
  • the sorting unit 310 may further sort the associated nodes and the candidate nodes after the candidate nodes whose relevance is less than the correlation threshold in each group from high to low.
  • the display unit 320 displays a sort result of a preset number or a high degree of relevance of the user selected number in each group identifier.
  • the specific process of the social network node grouping device is applied to the user friend and the user's potential friend grouping as described in the method, and the corresponding steps are performed by the corresponding modules, and details are not described herein again.
  • the combined output of the associated node and the candidate node in the group identifier according to the correlation degree is convenient for operation, the operation of adding the candidate node by the user is reduced, and the response efficiency of the system is improved.
  • a part of the candidate nodes can be hidden to save the display space; according to the display setting selected by the user, the corresponding associated nodes and candidate nodes are displayed, and the display is flexible.
  • the storage medium may be a magnetic disk, an optical disk, or a read-only storage memory (Read-Only) Memory, ROM) or Random Access Memory (RAM).

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Databases & Information Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • General Engineering & Computer Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本发明涉及一种社交网络节点分组方法及装置、计算机存储介质。该方法包括以下步骤：获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识；获取每个分组关联节点及候选节点与所述目标节点的相关度；根据所述相关度对每个分组内关联节点及候选节点进行组合输出。上述社交网络节点分组方法及装置、计算机存储介质，获取候选节点、关联节点及关联节点的分组标识，获取候选节点及每个分组标识中关联节点与目标节点的相关度，并按相关度对分组标识内关联节点和候选节点进行组合输出，方便操作，减少了用户添加候选节点的操作，提高了系统的响应效率。

Description

社交网络节点分组方法和装置、计算机存储介质
【技术领域】
本发明涉及互联网技术，特别是涉及一种社交网络节点分组方法和装置、计算机存储介质。
【背景技术】
互联网技术的发展促进了社交网络的兴起，社交网络是指所有能提供人与人之间联系的网站和产品，包括但不限于即时通信产品、社交网站、聊天室、BBS、虚拟社区、网络游戏等。社交网络中，用户可作为一个节点，用户之间存在直接好友关系或间接关系，即节点之间存在关联关系或者节点之间可能存在关联关系。社交网络提供节点之间可能存在关联关系，可促进节点之间发展其关系链，使得可能存在关联关系转变为存在关联关系。在社交网络中，将某一用户作为目标节点，与该目标节点存在关联关系的节点作为关联节点，与该目标节点存在潜在关联关系的节点作为候选节点。例如，关联节点为用户好友，候选节点为潜在好友，其中，潜在好友是指可能成为用户好友的用户，如此能为用户提供了潜在好友推荐，以促进用户发展其线上关系链。
传统的社交网络中一般是将“关联节点和候选节点”分成两大区块，分别展示给目标节点。目标节点只能和其存在关联关系的关联节点进行数据交互，如果要与存在潜在关联关系的候选节点交互，则需把候选节点变为关联节点。当目标节点需获取候选节点的数据时，则需去候选节点所在区块查找，操作不方便，且也不方便目标节点快速拓展其关系链。
【发明内容】
基于此，有必要提供一种操作方便的社交网络节点分组方法。
一种社交网络节点分组方法，包括以下步骤：
获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识；
获取每个分组关联节点及候选节点与所述目标节点的相关度；
根据所述相关度对每个分组关联节点及候选节点进行组合输出。
此外，还有必要提供一种操作方便的社交网络节点分组装置。
一种社交网络节点分组装置，包括：
提取模块，用于获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识；
处理模块，用于按分组标识获取每个分组关联节点及候选节点与所述目标节点的相关度；
输出模块，用于根据所述相关度对每个分组关联节点及候选节点进行组合输出。
此外，还有必要提供一种计算机存储介质。
一个或多个包含计算机可执行指令的计算机存储介质，所述计算机可执行指令用于执行一种社交网络节点分组方法，其特征在于，所述方法包括以下步骤：
获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识；
获取每个分组关联节点及候选节点与所述目标节点的相关度；
根据所述相关度对每个分组内关联节点及候选节点进行组合输出。
上述社交网络节点分组方法及装置、计算机存储介质，获取候选节点、关联节点及关联节点的分组标识，获取候选节点及每个分组标识中关联节点与目标节点的相关度，并按相关度对分组标识内关联节点和候选节点进行组合输出，方便操作，减少用户添加候选节点的操作，提高系统响应效率。
【附图说明】
图1为一个实施例中社交网络节点分组方法的流程示意图；
图2为一个实施例中步骤S20的具体流程示意图图；
图3为一个实施例中节点关系示意图；
图4为一个实施例中展示关联节点及候选节点的示意图；
图5为一个实施例中展示好友及潜在好友的示意图；
图6为一个实施例中社交网络节点分组装置的结构示意图；
图7为一个实施例中处理模块的内部结构示意图；
图8为一个实施例中输出模块的内部结构示意图；
图9为另一个实施例中社交网络节点分组装置的结构示意图。
【具体实施方式】
下面结合具体的实施例及附图对社交网络节点分组方法及装置的技术方案进行详细的描述，以使其更加清楚和完整。
如图1所示，在一个实施例中，一种社交网络节点分组方法，包括以下步骤：
步骤S10，获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及关联节点的分组标识。
具体的，社交网络中选取某一个用户作为目标节点，与用户存在好友关系的用户，即与目标节点存在关联关系的节点为关联节点；与用户可能存在好友关系的用户，即与目标节点存在潜在关联关系的节点为候选节点。其中，潜在关联关系是指候选节点将可能与目标节点存在关联关系。目标节点存在潜在关联关系的候选节点可预先获取得到，将候选节点放置候选节点列表中，然后从候选节点列表中获取目标节点对应的候选节点。候选节点列表中候选节点的获取方式多种。例如预设目标节点的属性信息的匹配权值，然后再将与目标节点不存在关联关系的节点的属性信息与目标节点的属性信息进行比对，得到与目标节点不存在关联关系的节点的权值，将权值大于阈值的与目标节点不存在关联关系的节点作为候选节点。节点的属性信息可包括性别、年龄、星座、血型、毕业学校、所学专业、毕业时间、籍贯、所在地、所从事的行业、兴趣爱好等真实信息；若为虚拟社交网络，还可包括虚拟世界中所在的地区、虚拟人物属性、虚拟人物等级等。
分组标识可包括初中同学、高中同学、大学同学、同事、家人等但不限于此。此外，分组标识还可为分组ID号，如001分组、002分组。
步骤S20，获取每个分组标识内关联节点及候选节点与目标节点的相关度。
具体的，按分组标识分别获取候选节点与目标节点的相关度、以及每个分组标识内关联节点与目标节点的相关度。相关度是指候选节点或关联节点与目标节点的属性信息匹配的程度。本实施例中，相关度可为用户与用户之间的属性信息相似程度。
在一个实施例中，如图2所示，步骤S20具体包括：
步骤S210，预设与组内节点存在关联关系的条件。
具体的，目标节点、关联节点和候选节点均为节点。统计节点与每个组内节点存在的关联关系。获取节点的相关度时分两种方式，第一种方式是组内的节点仅包括关联节点，获取节点的相关度时仅统计与节点存在关联关系的组内关联节点的个数；第二种是组内的节点包括关联节点和目标节点，获取节点的相关度时需统计与节点存在关联关系的组内的关联节点的个数及目标节点。
本实施例中，预设与组内节点存在关联关系的条件可包括以下至少一种：
（1）节点自身为组内节点。
具体的，如目标节点A的001分组内存在关联节点B，关联节点B为组内关联节点，关联节点B作为节点统计存在关联关系的组内节点个数时，关联节点B自身算一个。
（2）节点与组内节点存在关联关系。
具体的，如目标节点A的001分组内存在关联节点B、关联节点C、关联节点D，且关联节点B与关联节点C存在关联关系，关联节点B作为节点统计存在关联关系的001分组内节点个数时，关联节点C符合该条件，作为关联节点B的一个存在关联关系的001分组内节点。
（3）节点与预设数量的候选节点存在关联关系，且预设数量的候选节点与一个组内节点存在关联关系，则该节点与组内该节点存在关联关系。
具体的，组内关联节点即为用户的好友，候选节点即为可能成为用户的好友。目标节点A的001分组内存在关联节点B、关联节点C、关联节点D，分组外的候选节点为E、F、G、H，预设数量为3个，若E与F、G、H存在关联关系，B与F、G、H存在关联关系，则候选节点E作为节点统计存在关联关系的001分组内节点个数时，关联节点B符合该条件，将关联节点B作为候选节点E的一个存在关联关系的001分组内节点。
步骤S220，按照条件分别获取每个节点的存在关联关系的组内节点个数。
具体的，例如按照条件统计关联节点B的存在关联关系的001分组内节点个数，统计关联节点B的存在关联关系的002分组内节点个数。
步骤S230，将每个节点的存在关联关系的组内节点个数与组内节点个数的比值作为每个节点与目标节点的相关度。
为了进一步说明获取每个节点与目标节点的相关度的过程，如图3所示，设有一个与目标节点A存在关联关系的分组G1，其中有关联节点B1到B7共七个节点，有候选节点A1到A4共四个节点。预设数量为3个，按组内节点仅包括关联节点进行处理，则与A1存在关联关系的G1组内节点包括B1（符合条件（2））、B3（符合条件（2））、B6（符合条件（2）（3））、B5（符合条件（3）），因此其相关度为4/7；与B5存在关联关系的组内节点包括B5（符合条件（1））、B1（符合条件（2））、B2（符合条件（2））、B4（符合条件（2））、B7（符合条件（2））、B6（符合条件（3）），其相关度为6/7。按照组内节点包括关联节点获取相关度，有利于得到候选节点的相关度大于关联节点的相关度，若按照相关度从高到低排序，可得到较多的候选节点排在前面，方便对候选节点进行相关的操作。
若按照组内节点包括关联节点和目标节点，即目标节点作为获取相关度的一个统计节点。则与A1存在关联关系的G1组内节点包括B1（符合条件（2））、B3（符合条件（2））、B6（符合条件（2）（3））、B5（符合条件（3）），因此其相关度为4/8；与B5存在关联关系的组内节点包括B5（符合条件（1））、B1（符合条件（2））、B2（符合条件（2））、B4（符合条件（2））、B7（符合条件（2））、B6（符合条件（3）），目标节点A，其相关度为7/8。
步骤S30，根据相关度对每个分组标识内关联节点及候选节点进行组合输出。
具体的，可将每组内关联节点及候选节点任意组合，然后输出。
在一个实施例中，步骤S30具体为：根据相关度对每个分组标识内关联节点及候选节点进行排序；按分组标识展示排序后的关联节点及候选节点。
具体的，每个分组标识内，根据相关度对分组内关联节点及候选节点进行排序。如图3所示，分组标识G1，A1到A4的相关度分别为4/7、2/7、2/7、2/7，B1到B7的相关度分别为5/7、5/7、4/7、4/7、6/7、6/7、3/7，按相关度从高到低排序为B5、B6、B3、B4、A1、B7、A2、A3、A4。此外，也可按相关度从低到高排序。
对每一分组进行排序后，按分组标识展示排序后的关联节点及候选节点，如图4所示，展示界面上设有显示：关联节点、候选节点、全部的三个选择控件，关联节点列表：分组1中包括关联节点1、候选节点1，关联节点2、候选节点2、关联节点3、候选节点3和候选节点4，分组2中包括关联节点4、关联节点5、候选节点5和关联节点6。此外，关联节点和候选节点展示时，可标记不同的标识以区别，如图4中，关联节点前标记实线笑脸，候选节点标记虚线笑脸，该标识可由用户自行设定，或由系统设定。
另外，当候选节点在分组标识内的相关度为0时，在该分组标识内隐藏该候选节点或将该候选节点不加入该分组标识内。首先处理001分组时，候选节点H与目标节点的相关度为0，则候选节点H不加入001分组，再处理002分组时，候选节点H与目标节点的相关度不为0，则候选节点H加入002分组。
在其他实施例中，上述社交网络节点分组方法，根据相关度对每个分组标识内关联节点及候选节点进行排序的步骤具体为：按相关度从高到低对每个分组标识内关联节点及候选节点进行排序。
按分组标识展示排序后的关联节点及候选节点的步骤具体为：在每个分组标识内展示预设个数或用户选择数量的相关度高的排序结果。具体的，再参图3，分组G1，A1到A4的相关度分别为4/7、2/7、2/7、2/7，B1到B7的相关度分别为5/7、5/7、4/7、4/7、6/7、6/7、3/7，按相关度从高到低排序为、A3、A4，预设个数为7个，则展示B5、B6、B3、B4、A1、B7、A2。用户选择数量为6个，则展示B5、B6、B3、B4、A1、B7。此外，用户选择数量可由用户随时调整。
在其他实施例中，上述社交网络节点分组方法，在获取每个分组关联节点及候选节点与目标节点的相关度的步骤之后，还包括步骤：设置相关度阈值；隐藏相关度小于相关度阈值的候选节点。然后再根据相关度对每个分组内隐藏了相关度小于相关度阈值的候选节点后的关联节点及候选节点进行排序，并按照分组标识展示排序后的关联节点及候选节点。具体的，相关度阈值可由用户或系统根据需要设定，预先设置相关度阈值后，仅针对候选节点进行判断，当候选节点的相关度小于相关度阈值时，隐藏该候选节点，当大于等于时，显示该候选节点。对于目标节点的关联节点可全部展示给用户。此外，用户可根据需要随时设置相关度阈值，具体实现可如在界面上设置滑块控件，滑动该滑块调节相关度阈值。也可在展示排序后的关联节点及候选节点后，根据用户选择设置的相关度阈值，隐藏相关度小于相关度阈值的候选节点。
在其他实施例中，上述社交网络节点分组方法，还可以按相关度从高到低对每个分组内隐藏了相关度小于相关度阈值的候选节点后的关联节点及候选节点进行排序，并在每个分组标识内展示预设个数或用户选择数量的相关度高的排序结果。
在其他实施例中，上述社交网络节点分组方法，还包括步骤：获取用户选择的显示设置；根据显示设置及分组标识展示排序后的关联节点及候选节点。具体的，用户选择的显示设置可为仅显示关联节点、仅显示候选节点、显示全部中任意一种，则根据显示设置进行相应的展示。如仅显示关联节点，则显示B1至B7，仅显示候选节点，则显示A1至A4，显示全部，则展示排序后的所有关联节点及候选节点。
在一个实施例中，社交网络节点分组方法应用于用户好友及用户的潜在好友分组的具体步骤包括：
（a）获取用户的潜在好友、好友组别以及组内好友。
具体的，用户的潜在好友是指用户可能认识的人或可能成为好友的人。潜在好友即为候选节点，好友组别为分组标识，组内好友即为关联节点。可预先获取得到潜在好友列表，从潜在好友列表中获取用户的潜在好友。潜在好友列表中潜在好友的获取方式多种，例如预设用户的个人属性信息的匹配权值，然后再将非好友用户的个人属性信息与用户的个人属性信息进行比对，得到非好友用户的权值，将权值大于阈值的非好友用户作为潜在好友。用户的个人属性信息可包括性别、年龄、星座、血型、毕业学校、所学专业、毕业时间、籍贯、所在地、所从事的行业、兴趣爱好等真实信息；若为虚拟社交网络，还可包括虚拟世界中所在的地区、虚拟人物属性、虚拟人物等级等。
好友组别可包括初中同学、高中同学、大学同学、同事、家人等但不限于此。组内好友，例如用户A的初中同学组内初中同学用户B。
（b）获取每组好友及潜在好友与用户的相关度。
具体的，预设潜在好友及组内好友为节点，并预设与组内节点存在关系的条件。所述条件如上述描述的预设与分组标识内节点存在关联关系的条件，在此不再赘述。
（c）根据相关度对每组好友及潜在好友进行组合输出。
（c）具体为：（c1）根据相关度对每组好友及潜在好友进行排序。
（c2）按好友组别展示排序后的好友及潜在好友。
具体的，对每一组进行排序后，按好友组别展示排序后的好友及潜在好友，如图5所示，展示界面上设有显示：好友、潜在好友、全部的三个选择控件，好友列表：分组1中包括好友1、潜在好友1，好友2、潜在好友2、好友3、潜在好友4和潜在好友4，分组2中包括好友4、好友5、潜在好友5和好友6。
进一步的，社交网络节点分组方法应用于用户好友及用户的潜在好友分组中，在步骤（b）之后包括步骤：
（e）设置相关度阈值，隐藏相关度小于相关度阈值的潜在好友。
具体的，相关度阈值可由用户或系统根据需要设定，预先设置相关度阈值后，仅针对潜在好友进行判断，当潜在好友的相关度小于相关度阈值时，隐藏该潜在好友，当大于等于时，显示该潜在好友。对于用户的好友可全部展示给用户。此外，用户可根据需要随时设置相关度阈值，具体实现可如在界面上设置滑块控件，滑动该滑块调节相关度阈值。
进一步的，社交网络节点分组方法应用于用户好友及用户的潜在好友分组的具体步骤还包括：
（f）获取用户选择的显示设置，根据显示设置及好友组别展示排序后的好友及潜在好友。
具体的，用户选择的显示设置可为仅显示好友、仅显示潜在好友、显示全部中任意一种，则根据显示设置进行相应的展示。如仅显示好友，则显示好友1至好友6，仅显示潜在好友，则显示潜在好友1至潜在好友5，显示全部，则展示排序后的所有好友及潜在好友。
如图6所示，在一个实施例中，一种社交网络节点分组装置，包括提取模块10、处理模块20和输出模块30。其中：
提取模块10用于获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识。
具体的，社交网络中选取某一个用户作为目标节点，与用户存在好友关系的用户，即与目标节点存在关联关系的节点为关联节点；与用户可能存在好友关系的用户，即与目标节点存在潜在关联关系的节点为候选节点。与目标节点存在潜在关联关系的候选节点可预先获取得到，将候选节点放置候选节点列表中，然后从候选节点列表中获取目标节点对应的候选节点。候选节点列表中候选节点的获取方式多种。例如预设目标节点的属性信息的匹配权值，然后再将与目标节点不存在关联关系的节点的属性信息与目标节点的属性信息进行比对，得到与目标节点不存在关联关系的节点的权值，将权值大于阈值的与目标节点不存在关联关系的节点作为候选节点。节点的属性信息可包括性别、年龄、星座、血型、毕业学校、所学专业、毕业时间、籍贯、所在地、所从事的行业、兴趣爱好等真实信息；若为虚拟社交网络，还可包括虚拟世界中所在的地区、虚拟人物属性、虚拟人物等级等。
分组标识可包括初中同学、高中同学、大学同学、同事、家人等但不限于此。此外，分组标识还可为分组ID号，如001分组、002分组。
处理模块20用于获取每个分组标识内关联节点及候选节点与目标节点的相关度。具体的，按分组标识分别获取候选节点与目标节点的相关度、以及每个分组标识内关联节点与目标节点的相关度。相关度是指候选节点或关联节点与目标节点的属性信息匹配的程度。本实施例中，相关度可为用户与用户之间的属性信息相似程度。
在一个实施例中，如图7所示，处理模块20包括初始化单元210、统计单元220和获取单元230。其中：
初始化单元210用于预设与组内节点存在关联关系的条件。
具体的，目标节点、关联节点和候选节点均为节点。统计节点与每个分组标识内节点存在的关联关系。获取节点的相关度时分两种方式，第一种方式是组内的节点仅包括关联节点，获取节点的相关度时仅统计与节点存在关联关系的组内关联节点的个数；第二种是组内的节点包括关联节点和目标节点，获取节点的相关度时需统计与节点存在关联关系的组内的关联节点的个数及目标节点。
本实施例中，预设与组内关联存在关联关系的条件可包括以下至少一种：
（1）节点自身分为组内节点。
具体的，如目标节点A的001分组内存在关联节点B，关联节点B为001分组内关联节点，关联节点B作为节点统计存在关联关系的组内节点个数时，关联节点B自身算一个。
（2）节点与组内节点存在关联关系。
具体的，如目标节点A的001分组内存在关联节点B、关联节点C、关联节点D，且关联节点B与关联节点C存在关联关系，关联节点B作为节点统计存在关联关系的001分组内节点个数时，关联节点C符合该条件，作为关联节点B的一个存在关联关系的001分组内节点。
（3）节点与预设数量的候选节点存在关联关系，且预设数量的候选节点与一个组内节点存在关联关系，则该节点与组内该节点存在关联关系。
具体的，组内关联节点即为用户的好友，候选节点即为可能成为用户的好友。目标节点A的001分组内存在关联节点B、关联节点C、关联节点D，分组外的候选节点为E、F、G、H，预设数量为3个，若E与F、G、H存在关联关系，B与F、G、H存在关联关系，则候选节点E作为节点统计存在关联关系的001分组内节点个数时，关联节点B符合该条件，将关联节点B作为候选节点E的一个存在关联关系的001分组内节点。
统计单元220用于按照条件分别获取每个节点的存在关联关系的组内节点个数。具体的，按照条件统计关联节点B的存在关联关系的001分组内节点个数，统计关联节点B的存在关联关系的002分组内节点个数。
获取单元230用于将每个节点的存在关联关系的组内节点个数与组内节点个数的比值作为每个节点与目标节点的相关度。具体如图3所示，在此不再赘述。
若按照分组标识内节点包括关联节点和目标节点，即目标节点作为获取相关度的一个统计节点。则与A1存在关联关系的G1组内节点包括B1（符合条件（2））、B3（符合条件（2））、B6（符合条件（2）（3））、B5（符合条件（3）），因此其相关度为4/8；与B5存在关联关系的组内节点包括B5（符合条件（1））、B1（符合条件（2））、B2（符合条件（2））、B4（符合条件（2））、B7（符合条件（2））、B6（符合条件（3）），目标节点A，其相关度为7/8。
输出模块30用于根据所述相关度对每个分组标识内关联节点及候选节点进行组合输出。
具体的，输出模块30将每组内的关联节点及候选节点进行任意组合然后输出。
在一个实施例中，如图8所示，输出模块30包括排序单元310和展示单元320。其中：
排序单元310用于根据所述相关度对每个分组标识内关联节点及候选节点进行排序。具体的，每个分组标识内，根据相关度对分组标识内关联节点及候选节点进行排序。如图3所示，分组标识G1，A1到A4的相关度分别为4/7、2/7、2/7、2/7，B1到B7的相关度分别为5/7、5/7、4/7、4/7、6/7、6/7、3/7，按相关度从高到低排序为B5、B6、B3、B4、A1、B7、A2、A3、A4。此外，也可按相关度从低到高排序。
展示单元320用于按分组标识展示排序后的关联节点及候选节点。
具体的，对每一分组进行排序后，按分组标识展示排序后的关联节点及候选节点，如图4所示，展示界面上设有显示：关联节点、候选节点、全部的三个选择控件，关联节点列表：分组1中包括关联节点1、候选节点1，关联节点2、候选节点2、关联节点3、候选节点3和候选节点4，分组2中包括关联节点4、关联节点5、候选节点5和关联节点6。
此外，关联节点和候选节点展示时，可标记不同的标识以区别，如图4中，关联节点前标记实线笑脸，候选节点标记虚线笑脸，该标识可由用户自行设定，或由系统设定。
此外，当候选节点在分组标识内的相关度为0时，在该分组标识内隐藏该候选节点或将该候选节点不加入该该分组标识内。首先处理001分组时，候选节点H与目标节点的相关度为0，则候选节点H不加入001分组，再处理002分组时，候选节点H与目标节点的相关度不为0，则候选节点H加入002分组。
在其他实施例中，排序单元310还用于按相关度从高到低对每个分组内关联节点及候选节点进行排序；展示单元320还用于每个分组标识内展示预设个数或用户选择数量的相关度高的排序结果。具体的，再参图3，分组标识G1，A1到A4的相关度分别为4/7、2/7、2/7、2/7，B1到B7的相关度分别为5/7、5/7、4/7、4/7、6/7、6/7、3/7，按相关度从高到低排序为、A3、A4，预设个数为7个，则展示B5、B6、B3、B4、A1、B7、A2。用户选择数量为6个，则展示B5、B6、B3、B4、A1、B7。
如图9所示，在一个实施例中，上述社交网络节点分组装置，除了包括提取模块10、处理模块20和输出模块30，还包括预设模块40、判断模块50、隐藏模块60和输入模块70。其中：
预设模块40用于设置相关度阈值。具体的，相关度阈值可由用户或系统根据需要设定。
判断模块50用于判断候选节点的相关度是否小于相关度阈值。
隐藏模块60用于当候选节点的相关度小于相关度阈值时，隐藏该候选节点。
排序单元310还用于根据相关度对每个分组内隐藏了相关度小于相关度阈值的候选节点后的关联节点及候选节点进行排序。
展示单元320还用于展示隐藏了相关度小于相关度阈值的候选节点并根据相关度排序后的关联节点及候选节点。
输入模块70用于获取用户选择的显示设置。具体的，用户选择的显示设置可为仅显示关联节点、仅显示候选节点、显示全部中任意一种，则根据显示设置进行相应的展示。如仅显示关联节点，则显示B1至B7，仅显示候选节点，则显示A1至A4，显示全部，则展示排序后的所有关联节点及候选节点。
展示单元320还用于根据显示设置及分组标识展示排序后的关联节点及候选节点。
在其他实施例中，上述社交网络节点分组装置，排序单元310还可以按相关度从高到低对每个分组内隐藏了相关度小于相关度阈值的候选节点后的关联节点及候选节点进行排序，展示单元320在每个分组标识内展示预设个数或用户选择数量的相关度高的排序结果。
社交网络节点分组装置应用于用户好友及用户的潜在好友分组的具体过程如方法中所描述，对应的步骤由相应的模块完成，在此不再赘述。
上述社交网络节点分组方法及装置，获取目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点及关联节点的分组标识，获取候选节点及每个分组标识内关联节点的相关度，并按相关度对分组标识内关联节点和候选节点进行组合输出，方便操作，减少了用户添加候选节点的操作，提高了系统的响应效率。
另外，设置相关度阈值，可将一部分候选节点隐藏，节省展示空间；根据用户选择的显示设置，展示相应的关联节点及候选节点，展示灵活。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体（Read-Only Memory，ROM）或随机存储记忆体（Random Access Memory，RAM）等。
以上所述实施例仅表达了本发明的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对本发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。

Claims (21)

  1. 一种社交网络节点分组方法，包括以下步骤：
    获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识；
    获取每个分组关联节点及候选节点与所述目标节点的相关度；
    根据所述相关度对每个分组内关联节点及候选节点进行组合输出。
  2. 根据权利要求1所述的社交网络节点分组方法，其特征在于，所述获取每个分组关联节点及候选节点与目标节点的相关度的步骤包括：
    预设与所述组内节点存在关联关系的条件；
    按照所述条件分别获取每个节点的存在关联关系的组内节点个数；
    将每个节点的存在关联关系的组内节点个数与组内节点个数的比值作为每个节点与目标节点的相关度。
  3. 根据权利要求2所述的社交网络节点分组方法，其特征在于，所述预设与所述组内节点存在关联关系的条件包括以下至少一种：
    节点自身为组内节点；
    节点与组内节点存在关联关系；
    节点与预设数量的候选节点存在直接关联关系，且所述预设数量的候选节点与一个组内节点存在关联关系，则所述节点与所述组内所述节点存在关联关系。
  4. 根据权利要求1至3中任一项所述的社交网络节点分组方法，其特征在于，所述根据所述相关度对每个分组内关联节点及候选节点进行组合输出的步骤具体为：
    根据所述相关度对每个分组内关联节点及候选节点进行排序；
    按分组标识展示排序后的关联节点及候选节点。
  5. 根据权利要求4所述的社交网络节点分组方法，其特征在于，在所述根据对每个分组内关联节点及候选节点进行排序的步骤之前还包括步骤：
    根据预先设置相关度阈值，隐藏相关度小于所述相关度阈值的候选节点；
    所述根据对每个分组内关联节点及候选节点进行排序的步骤具体为：
    根据所述相关度对每个分组内隐藏了相关度小于相关度阈值的候选节点后的关联节点及候选节点进行排序。
  6. 根据权利要求4所述的社交网络节点分组方法，其特征在于，所述根据所述相关度对每个分组内关联节点及候选节点进行排序的步骤具体为：
    按相关度从高到低对每个分组标识内关联节点及候选节点进行排序；
    所述按所述分组标识展示排序后的关联节点及候选节点的步骤具体为：
    在每个分组标识内展示预设个数或用户选择数量的相关度高的排序结果。
  7. 根据权利要求4所述的社交网络节点分组方法，其特征在于，还包括步骤：
    获取用户选择的显示设置；
    根据所述显示设置及所述分组标识展示排序后的关联节点及候选节点。
  8. 一种社交网络节点分组装置，其特征在于，包括：
    提取模块，用于获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识；
    处理模块，用于获取每个分组关联节点及候选节点与所述目标节点的相关度；
    输出模块，用于根据所述相关度对每个分组关联节点及候选节点进行组合输出。
  9. 根据权利要求8所述的社交网络节点分组装置，其特征在于，所述处理模块包括：
    初始化单元，用于预设与所述组内节点存在关联关系的条件，；
    统计单元，用于按照所述条件分别获取每个节点的存在关联关系的分组标识内节点个数；
    获取单元，用于将每个节点的存在关联关系的组内节点个数与组内节点个数的比值作为每个节点与目标节点的相关度。
  10. 根据权利要求9所述的社交网络节点分组装置，其特征在于，所述初始化单元预设与所述组内节点存在关联关系的条件包括以下至少一种：
    节点自身为组内节点；
    节点与组内节点存在关联关系；
    节点与预设数量的候选节点存在关联关系，且所述预设数量的候选节点与一个组内节点存在直接关联关系，则所述节点与所述组内所述节点存在关联关系。
  11. 根据权利要求8至10中任一项所述的社交网络节点分组装置，其特征在于，所述输出模块包括：
    排序单元，根据所述相关度对每个分组内关联节点及候选节点进行排序；
    展示单元，用于按所述分组标识展示排序后的关联节点及候选节点。
  12. 根据权利要求11所述的社交网络节点分组装置，其特征在于，还包括：
    预设模块，用于设置相关度阈值；
    判断模块，用于判断候选节点的相关度是否小于所述相关度阈值；
    隐藏模块，用于隐藏相关度小于所述相关度阈值的候选节点；
    所述排序单元还用于根据所述相关度对每个分组内隐藏了相关度小于相关度阈值的候选节点后的关联节点及候选节点进行排序。
  13. 根据权利要求11所述的社交网络节点分组装置，其特征在于，所述排序单元还用于按相关度从高到低对每个分组内关联节点及候选节点进行排序；所述展示单元还用于在每个分组标识内展示预设个数或用户选择数量的相关度高的排序结果。
  14. 根据权利要求11所述的社交网络节点分组装置，其特征在于，还包括：
    输入模块，用于获取用户选择的显示设置；
    所述展示单元还用于根据所述显示设置及所述分组标识展示排序后的关联节点及候选节点。
  15. 一个或多个包含计算机可执行指令的计算机存储介质，所述计算机可执行指令用于执行一种社交网络节点分组方法，其特征在于，所述方法包括以下步骤：
    获取与目标节点存在潜在关联关系的候选节点、与目标节点存在关联关系的关联节点以及所述关联节点的分组标识；
    获取每个分组关联节点及候选节点与所述目标节点的相关度；
    根据所述相关度对每个分组内关联节点及候选节点进行组合输出。
  16. 根据权利要求15所述的计算机存储介质，其特征在于，所述获取每个分组关联节点及候选节点与目标节点的相关度的步骤包括：
    预设与所述组内节点存在关联关系的条件；
    按照所述条件分别获取每个节点的存在关联关系的组内节点个数；
    将每个节点的存在关联关系的组内节点个数与组内节点个数的比值作为每个节点与目标节点的相关度。
  17. 根据权利要求16所述的计算机存储介质，其特征在于，所述预设与所述组内节点存在关联关系的条件包括以下至少一种：
    节点自身为组内节点；
    节点与组内节点存在关联关系；
    节点与预设数量的候选节点存在直接关联关系，且所述预设数量的候选节点与一个组内节点存在关联关系，则所述节点与所述组内所述节点存在关联关系。
  18. 根据权利要求15至17中任一项所述的计算机存储介质，其特征在于，所述根据所述相关度对每个分组内关联节点及候选节点进行组合输出的步骤具体为：
    根据所述相关度对每个分组内关联节点及候选节点进行排序；
    按分组标识展示排序后的关联节点及候选节点。
  19. 根据权利要求18所述的计算机存储介质，其特征在于，在所述根据对每个分组内关联节点及候选节点进行排序的步骤之前还包括步骤：
    根据预先设置相关度阈值，隐藏相关度小于所述相关度阈值的候选节点；
    所述根据对每个分组内关联节点及候选节点进行排序的步骤具体为：
    根据所述相关度对每个分组内隐藏了相关度小于相关度阈值的候选节点后的关联节点及候选节点进行排序。
  20. 根据权利要求19所述的计算机存储介质，其特征在于，所述根据所述相关度对每个分组内关联节点及候选节点进行排序的步骤具体为：
    按相关度从高到低对每个分组标识内关联节点及候选节点进行排序；
    所述按所述分组标识展示排序后的关联节点及候选节点的步骤具体为：
    在每个分组标识内展示预设个数或用户选择数量的相关度高的排序结果。
  21. 根据权利要求19所述的计算机存储介质，其特征在于，还包括步骤：
    获取用户选择的显示设置；
    根据所述显示设置及所述分组标识展示排序后的关联节点及候选节点。
PCT/CN2013/071038 2012-05-15 2013-01-28 社交网络节点分组方法和装置、计算机存储介质 WO2013170643A1 (zh)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2015511906A JP5946956B2 (ja) 2012-05-15 2013-01-28 ソーシャルネットワークのノードをグループ化する方法、装置、およびコンピュータ記憶媒体
US14/401,271 US10169476B2 (en) 2012-05-15 2013-01-28 Method, apparatus, and computer-readable storage medium for grouping social network nodes
KR1020147035105A KR101678115B1 (ko) 2012-05-15 2013-01-28 소셜 네트워크 노드 그루핑 방법, 장치 및 컴퓨터 저장 매체
US16/197,231 US11361045B2 (en) 2012-05-15 2018-11-20 Method, apparatus, and computer-readable storage medium for grouping social network nodes

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201210149998.2A CN103428165B (zh) 2012-05-15 2012-05-15 社交网络节点分组方法和装置
CN201210149998.2 2012-05-15

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US14/401,271 A-371-Of-International US10169476B2 (en) 2012-05-15 2013-01-28 Method, apparatus, and computer-readable storage medium for grouping social network nodes
US16/197,231 Continuation US11361045B2 (en) 2012-05-15 2018-11-20 Method, apparatus, and computer-readable storage medium for grouping social network nodes

Publications (1)

Publication Number Publication Date
WO2013170643A1 true WO2013170643A1 (zh) 2013-11-21

Family

ID=49583085

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2013/071038 WO2013170643A1 (zh) 2012-05-15 2013-01-28 社交网络节点分组方法和装置、计算机存储介质

Country Status (5)

Country Link
US (2) US10169476B2 (zh)
JP (1) JP5946956B2 (zh)
KR (1) KR101678115B1 (zh)
CN (1) CN103428165B (zh)
WO (1) WO2013170643A1 (zh)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10380629B2 (en) * 2012-05-25 2019-08-13 Microsoft Technology Licensing, Llc Leveraging a social graph to deliver relevant recommendations
CN104052651B (zh) * 2014-06-03 2017-09-12 西安交通大学 一种建立社交组的方法和装置
CN105426375B (zh) * 2014-09-22 2019-01-18 阿里巴巴集团控股有限公司 一种关系网络的计算方法及装置
CN106357418B (zh) * 2015-07-15 2019-08-30 阿里巴巴集团控股有限公司 一种基于复杂网络的特征提取方法及装置
CN110910242B (zh) * 2018-09-14 2022-12-02 北京布洛城科技有限公司 区块链生成方法、装置、终端设备及存储介质
CN109408543B (zh) * 2018-09-26 2021-07-23 北京华宝智慧科技有限公司 一种智能关系网嗅探方法
CN109446359A (zh) * 2018-09-26 2019-03-08 深圳壹账通智能科技有限公司 一种图形数据库构建方法及终端设备
CN111046237B (zh) * 2018-10-10 2024-04-05 京东科技控股股份有限公司 用户行为数据处理方法、装置、电子设备及可读介质
CN110022270B (zh) * 2019-03-27 2020-07-10 阿里巴巴集团控股有限公司 具有多种资源类型的资源分配方法和装置
US20220180420A1 (en) * 2019-03-28 2022-06-09 Nec Corporation Intermediary device, control device, control method and storage medium
CN110335164A (zh) * 2019-06-26 2019-10-15 腾讯科技（深圳）有限公司 人际关系信息生成方法和装置
CN112333085B (zh) * 2020-10-30 2023-02-03 维沃移动通信有限公司 社交方法和电子设备

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1848839A (zh) * 2005-01-07 2006-10-18 三星电子株式会社 在网络中对装置可提供的任务区分优先次序的方法和系统
US20090177744A1 (en) * 2008-01-04 2009-07-09 Yahoo! Inc. Identifying and employing social network relationships
CN102270085A (zh) * 2010-06-04 2011-12-07 腾讯科技（深圳）有限公司 即时通讯工具中搜索联系人的方法及即时通讯客户端
CN102377683A (zh) * 2010-08-04 2012-03-14 腾讯科技（深圳）有限公司 一种向即时通讯用户推荐联系人信息的方法和终端

Family Cites Families (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4260702B2 (ja) * 2004-07-28 2009-04-30 株式会社データサーフ 対象関連表示装置および対象関連表示プログラム
US8249903B2 (en) * 2006-10-10 2012-08-21 Accenture Global Services Limited Method and system of determining and evaluating a business relationship network for forming business relationships
JP2010093734A (ja) 2008-10-10 2010-04-22 Toshiba Corp 映像視聴装置、映像視聴装置の制御方法
CN101399785A (zh) * 2008-10-16 2009-04-01 腾讯科技（深圳）有限公司 Im平台好友列表展现系统及展现方法
CN101931707A (zh) * 2010-09-27 2010-12-29 北京开心人信息技术有限公司 一种移动通讯设备即时交互中查找联系人的方法及装置
US8321417B2 (en) * 2010-10-14 2012-11-27 6464076 Canada Inc. Method of visualizing the collective opinion of a group
CN101968818A (zh) * 2010-11-08 2011-02-09 北京开心人信息技术有限公司 一种社交网站中向用户推荐好友的方法及系统
US8606776B2 (en) * 2011-02-18 2013-12-10 Google Inc. Affinity based ranked for search and display
US8838601B2 (en) * 2011-08-31 2014-09-16 Comscore, Inc. Data fusion using behavioral factors
US9727924B2 (en) * 2011-10-10 2017-08-08 Salesforce.Com, Inc. Computer implemented methods and apparatus for informing a user of social network data when the data is relevant to the user
US20170178157A1 (en) * 2012-03-07 2017-06-22 Google Inc. Targeting content to users in groups
CN103377180A (zh) * 2012-04-28 2013-10-30 国际商业机器公司 数据粘贴的方法及装置
US9407719B2 (en) * 2013-01-30 2016-08-02 Linkedin Corporation Batch connect
US10210261B2 (en) * 2014-06-18 2019-02-19 Facebook, Inc. Ranking and filtering groups recommendations
US10074122B2 (en) * 2014-06-30 2018-09-11 Microsoft Technology Licensing, Llc Account recommendations
US20160224683A1 (en) * 2015-01-30 2016-08-04 Cory Michael Hicks Blending of content item types in a social network
US10191988B2 (en) * 2015-10-28 2019-01-29 Sony Mobile Communications Inc. System and method for returning prioritized content
US10542023B2 (en) * 2017-11-21 2020-01-21 International Business Machines Corporation Detecting compromised social media accounts by analyzing affinity groups

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1848839A (zh) * 2005-01-07 2006-10-18 三星电子株式会社 在网络中对装置可提供的任务区分优先次序的方法和系统
US20090177744A1 (en) * 2008-01-04 2009-07-09 Yahoo! Inc. Identifying and employing social network relationships
CN102270085A (zh) * 2010-06-04 2011-12-07 腾讯科技（深圳）有限公司 即时通讯工具中搜索联系人的方法及即时通讯客户端
CN102377683A (zh) * 2010-08-04 2012-03-14 腾讯科技（深圳）有限公司 一种向即时通讯用户推荐联系人信息的方法和终端

Also Published As

Publication number Publication date
CN103428165B (zh) 2015-07-01
CN103428165A (zh) 2013-12-04
JP2015523628A (ja) 2015-08-13
US11361045B2 (en) 2022-06-14
JP5946956B2 (ja) 2016-07-06
US10169476B2 (en) 2019-01-01
KR101678115B1 (ko) 2016-11-21
KR20150007353A (ko) 2015-01-20
US20190087505A1 (en) 2019-03-21
US20150134663A1 (en) 2015-05-14

Similar Documents

Publication Publication Date Title
WO2013170643A1 (zh) 社交网络节点分组方法和装置、计算机存储介质
WO2018113241A1 (zh) 页面展示方法、装置、服务器和存储介质
Ratkiewicz et al. Detecting and tracking political abuse in social media
WO2015003480A1 (zh) 一种社交媒体中的信息推荐方法和装置
US9516050B2 (en) Monitoring propagation in a network
WO2017020628A1 (zh) 动态信息显示方法、服务器和终端
WO2018090544A1 (zh) DoS/DDoS攻击检测方法和装置、服务器和存储介质
WO2019037197A1 (zh) 主题分类器的训练方法、装置及计算机可读存储介质
WO2016125949A1 (ko) 문서 자동 요약 방법 및 서버
WO2013097603A1 (zh) 评价信息生成方法及系统、计算机存储介质
WO2020164281A1 (zh) 基于文字定位识别的表格解析方法、介质及计算机设备
WO2017054463A1 (zh) 事件信息推送方法、事件信息推送装置及存储介质
WO2011088723A1 (zh) Sns社区中推荐好友的方法及系统
WO2017071363A1 (zh) 密码的共享方法、密码的共享系统及终端设备
EP3031213A1 (en) Apparatus, server, and method for providing conversation topic
WO2021095987A1 (ko) 다중타입 엔티티에 기반한 지식 보완 방법 및 장치
US9218426B1 (en) Apparatus and method for personalized delivery of content from multiple data sources
WO2015129983A1 (ko) 분산 퍼지 연관 규칙 마이닝에 기반한 영화 추천 장치 및 방법
Legendre et al. Phylogenetic analyses of termite post‐embryonic sequences illuminate caste and developmental pathway evolution
CN105721288A (zh) 一种在线的精准交际系统及其方法
WO2023024970A1 (zh) 信息处理方法、相关设备及可读存储介质
CN108132742A (zh) 一种终端设备及其关联窗口的显示方法
WO2008131698A1 (fr) Procédé de recherche d'informations et dispositif correspondant
WO2014029257A1 (zh) 网络组图聚合方法、系统及图片搜索方法、系统
WO2015137585A1 (ko) 채터봇을 이용한 온라인 채팅 커뮤니티 운용방법

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 13790627

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2015511906

Country of ref document: JP

Kind code of ref document: A

WWE Wipo information: entry into national phase

Ref document number: 14401271

Country of ref document: US

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 20147035105

Country of ref document: KR

Kind code of ref document: A

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 01/04/2015)

122 Ep: pct application non-entry in european phase

Ref document number: 13790627

Country of ref document: EP

Kind code of ref document: A1