CN104182422A - Unified address book information processing method and system - Google Patents
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Abstract
The invention provides a unified address book information processing method and system, and relates to the technical field of mobile Internet. According to the method, a unified address book is abstracted into a multi-layer graphical model structure, the relationships of contacts, friends and the like among users are overlaid into a unified network graphic, and the problem that the transmissibility of the friend relation cannot be expressed in the conventional vector space model is solved through the communicated multi-layer graph structure. According to the technical scheme of the invention, possible noise information existing in a contact relation graph is removed by using a contact correlation factor, the social relations among the users can be effectively mined through an improved restart type random walk algorithm, and business cross marketing popularization and the derivation of other services are facilitated.
Description
Technical field
The present invention relates to mobile internet technical field, particularly a kind of unified address list information disposal route and system.
Background technology
Along with the explosive growth of internet, the business relevant to user contact person, social networks is more and more, is thereupon that the business demands such as the expansion of good friend's relation, groups of users division all need user social contact relation to excavate; Current internet individual subscriber social networks information dispersion, lacks effective means simultaneously individual subscriber social networks is carried out to degree of depth excavation, unifies to have contained abundant user social contact relation in address list, imperative to unifying address list modeling and carrying out data mining.
Current social networks method for digging passes through the proper vector of vector space model structuring user's mostly, then calculates two similarities between user; Also have certain methods to utilize once good friend to carry out being related to expansion.There are following problems in existing social networks digging technology: vector space model only calculates two similarities between user, does not consider the transitivity of social networks; As QQ circle etc. only only limits to once good friend to the excavation of social networks, do not do darker analysis, cause the good friend and the user that recommend in fact to there is no social networks, there are a large amount of noise informations in the result of relation excavation.
Summary of the invention
The present inventor finds to have problems in above-mentioned prior art, and has therefore proposed a kind of new technical scheme at least one problem in described problem.
An object of the present invention is to provide a kind of technical scheme for unified address list information processing.
According to a first aspect of the invention, provide a kind of unified address list information disposal route, having comprised:
Build multilayer graph of a relation according to the contact person of user, user in unified address list, user's friend information, wherein each user, user's contact person, user's good friend is respectively as the node of multilayer graph of a relation;
In multilayer graph of a relation, between user node and user's contact attribute node and good friend's attribute node of user, generate respectively limit; To between good friend's attribute node of good friend's user each other, generate limit; To between contact person's user's each other contact attribute node, determine between user's contact attribute node and generate limit according to contact person's association factor;
Generate adjacency matrix according to the annexation of multilayer graph of a relation;
Determine the probability matrix of steady state (SS) by restarting type Random Walk Algorithm based on adjacency matrix according to initial probability matrix.
Alternatively, contact person's association factor is determined according to following formula:
Wherein, A, B are user node, and C (A ∩ B) is the quantity of the common contacts of A and B, and C (A), C (B) are A, B contact person's number separately, and N is contact person's number altogether in unified address list.
Alternatively, will between contact person's user's each other contact attribute node, determine that according to contact person's association factor between user's contact attribute node, generating limit comprises:
Determine contact person's user's the internodal contact person's association factor of contact attribute each other;
If contact person's association factor is greater than setting threshold, between by contact person's user's each other contact attribute node, generate limit.
Alternatively, generating adjacency matrix according to the annexation of multilayer graph of a relation comprises:
Generate adjacency matrix according to the annexation of multilayer graph of a relation, wherein, weight for the limit between user node and user's contact attribute node and good friend's attribute node of user is 1, being 1 for the weight on the limit between good friend's attribute node, is normalized contact person's association factor for the weight on the limit between contact attribute node;
Adjacency matrix is normalized by row.
Alternatively, the probability matrix of determining steady state (SS) by restarting type Random Walk Algorithm based on adjacency matrix according to initial probability matrix comprises:
Generate initial matrix μ
0for N*1 rank matrix, making seed user node initial value is 1, and other node initial values are 0;
Initialization μ=μ
0;
Carry out following iteration:
μ
t+1=(1-λ) M μ
t+ λ μ
0, wherein 0< λ <1, M is adjacency matrix
Exit iteration until stable;
Obtain the probability matrix μ * of steady state (SS).
Alternatively, the method also comprises: put to the proof the tightness degree of calculating between kind of child user and other users according to the probability of steady state (SS).
According to a further aspect in the invention, provide a kind of unified address list information disposal system, comprising:
Graph of a relation node generation module, for building multilayer graph of a relation according to unified address list user, user's contact person, user's friend information, wherein each user, user's contact person, user's good friend is respectively as the node of multilayer graph of a relation;
Graph of a relation limit generation module at multilayer graph of a relation, generates respectively limit between user node and user's contact attribute node and good friend's attribute node of user; To between good friend's attribute node of good friend's user each other, generate limit; To between contact person's user's each other contact attribute node, determine between user's contact attribute node and generate limit according to contact person's association factor;
Adjacency matrix generation module, for generating adjacency matrix according to the annexation of multilayer graph of a relation;
Probability matrix determination module, for determining the probability matrix of steady state (SS) by restarting type Random Walk Algorithm based on adjacency matrix according to initial probability matrix.
Alternatively, contact person's association factor is determined according to following formula:
Wherein, A, B are user node, and C (A ∩ B) is the quantity of the common contacts of A and B, and C (A), C (B) are A, B contact person's number separately, and N is contact person's number altogether in unified address list.
Alternatively, graph of a relation limit generation module comprises:
Characteristic edge generation unit, for generating respectively limit between the contact attribute node user node and user and good friend's attribute node of user;
Good friend limit generation unit, for generating limit between good friend's attribute node of good friend's user each other;
Contact person limit generation unit for determining contact person's user's the internodal contact person's association factor of contact attribute each other, if contact person's association factor is greater than setting threshold, generates limit between by contact person's user's each other contact attribute node.
Alternatively, adjacency matrix generation module generates adjacency matrix according to the annexation of multilayer graph of a relation, wherein, weight for the limit between user node and user's contact attribute node and good friend's attribute node of user is 1, being 1 for the weight on the limit between good friend's attribute node, is normalized contact person's association factor for the weight on the limit between contact attribute node; Adjacency matrix is normalized by row.
Alternatively, probability matrix determination module is used for generating initial matrix μ
0for N*1 rank matrix, making seed user node initial value is 1, and other node initial values are 0; Initialization μ=μ
0, carry out following iteration:
μ
t+1=(1-λ) M μ
t+ λ μ
0, wherein 0< λ <1, M is adjacency matrix
Exit iteration until stable, obtain the probability matrix μ * of steady state (SS).
An advantage of the present invention is, to unify address list abstract is multilayer graph model structure, the mutual relationship such as contact person, good friend between user is superposed to a unified network chart, has solved the transitivity of good friend's relation that traditional vector space model is beyond expression by the multilayer graph structure being communicated with; Utilize contact person's association factor to remove the noise information that may exist in contact relationship figure, obtain stable probability matrix by restarting type Random Walk Algorithm.
By the detailed description to exemplary embodiment of the present invention referring to accompanying drawing, it is clear that further feature of the present invention and advantage thereof will become.
Brief description of the drawings
The accompanying drawing that forms a part for instructions has been described embodiments of the invention, and together with the description for explaining principle of the present invention.
With reference to accompanying drawing, according to detailed description below, can more be expressly understood the present invention, wherein:
Fig. 1 illustrates according to the process flow diagram of unified address list information disposal route of the present invention embodiment.
Fig. 2 illustrates that basis the present invention is based on the schematic diagram of an example of the unified address list modeling of multilayer graph model.
Fig. 3 illustrates according to the process flow diagram of another embodiment of unified address list information disposal route of the present invention.
Fig. 4 illustrates according to the structural drawing of unified address list information disposal system of the present invention embodiment.
Fig. 5 illustrates according to the structural drawing of another embodiment of unified address list information disposal system of the present invention.
Embodiment
Describe various exemplary embodiment of the present invention in detail now with reference to accompanying drawing.It should be noted that: unless illustrate in addition, the parts of setting forth in these embodiments and positioned opposite, numeral expression formula and the numerical value of step do not limit the scope of the invention.
, it should be understood that for convenience of description, the size of the various piece shown in accompanying drawing is not to draw according to actual proportionate relationship meanwhile.
Illustrative to the description only actually of at least one exemplary embodiment below, never as any restriction to the present invention and application or use.
May not discuss in detail for the known technology of person of ordinary skill in the relevant, method and apparatus, but in suitable situation, described technology, method and apparatus should be regarded as authorizing a part for instructions.
In all examples with discussing shown here, it is exemplary that any occurrence should be construed as merely, instead of as restriction.Therefore, other example of exemplary embodiment can have different values.
It should be noted that: in similar label and letter accompanying drawing below, represent similar terms, therefore, once be defined in an a certain Xiang Yi accompanying drawing, in accompanying drawing subsequently, do not need it to be further discussed.
Fig. 1 illustrates according to the process flow diagram of unified address list information disposal route of the present invention embodiment.
As shown in Figure 1, step 102, builds multilayer graph of a relation according to the contact person of user, user in unified address list, user's friend information, and wherein each user, user's contact person, user's good friend is respectively as the node of multilayer graph of a relation.
Step 104 in multilayer graph of a relation, generates respectively limit between user node and this user's contact attribute node and good friend's attribute node of this user; To good friend's category node, will between good friend's attribute node of good friend's user each other, generate limit; For contact feature node, will between contact person's user's each other contact attribute node, determine between user's contact attribute node and generate limit according to contact person's association factor.
Step 106, generates adjacency matrix according to the annexation of multilayer graph of a relation.
Step 108, determines the probability matrix of steady state (SS) by restarting type Random Walk Algorithm based on adjacency matrix according to initial probability matrix.
In above-described embodiment, to unify address list abstract is multilayer graph model structure, the mutual relationship such as contact person, good friend between user is superposed to a unified network chart, has solved the transitivity of good friend's relation that traditional vector space model is beyond expression by the multilayer graph structure being communicated with; Utilize contact person's association factor to remove the noise information that may exist in contact relationship figure, obtain stable probability matrix by restarting type Random Walk Algorithm.
For multilayer graph of a relation G, wherein comprise three category nodes: user node I, user's contact attribute node C, good friend's attribute node F of user.Fig. 2 shows the sample of multilayer graph of a relation.I1, I2, I3, I4 are the user nodes in unified address list, wherein I4 is start node, what calculate is the degree of association of other all nodes and I4, C1, C2, C3, C4 be respectively each user contact attribute node, F1, F2, F3, F4 are respectively good friend's attribute nodes of each user.
Fig. 3 illustrates according to the process flow diagram of another embodiment of unified address list information disposal route of the present invention.
As shown in Figure 3, step 301, the node of generation multilayer graph of a relation.
Each user node is designated as V (Ii), and contact attribute node is designated as to V (Ci), and good friend's characteristic node is designated as V (Fi).
Step 302, according to the limit of the type structure multilayer graph of a relation of node.
Wherein, step 302a for example Ii, by directly constructing a limit between example and its each feature, constructs a limit between V (Ii) and V (Ci), between V (Ii) and V (Fi), constructs a limit.
Step 302b sets up associatedly between example node, need to realize by build limit between the characteristic node of example, to good friend's category node V (Fi) if two node social good friends each other build a limit.
Step 302c, for contact feature node V (Ci), in traditional algorithm, directly contact person's node is each other built to a limit, in fact in actual life, contact person's scene is very complicated each other, likely that the people that the degree of association is not high adds as mutually mobile phone contact, if this class relation is set up to limit, can produce very large noise.In this embodiment, filter out this kind of noise by contact person's association factor CR (A, B), what contact person's association factor was greater than setting threshold builds a limit between the contact attribute node of A, B, wherein
A, B are user node, and C (A ∩ B) is the quantity of the common contacts of A and B, and C (A), C (B) are A, B contact person's number separately, and wherein N is contact person's number altogether in unified address list.The weight of the total contact person of contact person's association factor utilization between user filters out the contact relationship of noise.
Finally obtain multilayer graph of a relation G, wherein have three class limits:
(1) limit between user node and its characteristic node;
(2) limit between contact feature node;
(3) limit between good friend's relationship characteristic node.
Step 303, for multilayer graph of a relation arranges weight.
Multilayer graph of a relation G is a undirected weighted graph, and the weight on first and third class limit is 1, and the weight on Equations of The Second Kind limit is the value after the normalization of contact person's association factor.
For graph of a relation G, suppose that node in G has N, to obtain probability vector μ * under steady state (SS)=(μ * (1) ..., μ * (N)).
Provide the algorithm of evaluate candidate example below:
Step 304, definition initial matrix μ
0, for N*1 rank matrix, to μ
0be normalized, order kind of a child user initial value is in 1(Fig. 2, to be I4), other types node initial value is 0.
Step 305, generates adjacency matrix.For graph of a relation G, generate adjacency matrix M, M is normalized by row, making the each row sum in M is 1.
Step 306, initialization μ=μ 0, t=0;
Step 307, iteration:
μ
t+1=(1-λ) M μ
t+ λ μ
0, wherein 0< λ <1(2)
Does step 308, judge that μ reaches steady state (SS)? if not, t=t+1, continues step 307, otherwise, continue step 309.
Step 309, according to the probability matrix μ * of final steady state (SS), calculates the tightness degree between kind of child user and other users.
Fig. 4 illustrates according to the structural drawing of unified address list information disposal system of the present invention embodiment.As shown in Figure 4, this system comprises: graph of a relation node generation module 41, for building multilayer graph of a relation according to unified address list user, user's contact person, user's friend information, wherein each user, user's contact person, user's good friend is respectively as the node of multilayer graph of a relation; Graph of a relation limit generation module 42 at multilayer graph of a relation, generates respectively limit between user node and user's contact attribute node and good friend's attribute node of user; To between good friend's attribute node of good friend's user each other, generate limit; To between contact person's user's each other contact attribute node, determine between user's contact attribute node and generate limit according to contact person's association factor; Adjacency matrix generation module 43, for generating adjacency matrix according to the annexation of multilayer graph of a relation; Probability matrix determination module 44, for determining the probability matrix of steady state (SS) by restarting type Random Walk Algorithm based on adjacency matrix according to initial probability matrix.
In one embodiment, contact person's association factor is determined according to following formula:
Wherein, A, B are user node, and C (A ∩ B) is the quantity of the common contacts of A and B, and C (A), C (B) are A, B contact person's number separately, and N is contact person's number altogether in unified address list.
In one embodiment, adjacency matrix generation module generates adjacency matrix according to the annexation of multilayer graph of a relation, wherein, weight for the limit between user node and user's contact attribute node and good friend's attribute node of user is 1, being 1 for the weight on the limit between good friend's attribute node, is normalized contact person's association factor for the weight on the limit between contact attribute node; Adjacency matrix is normalized by row;
In one embodiment, probability matrix determination module is used for generating initial matrix μ
0for N*1 rank matrix, making seed user node initial value is 1, and other node initial values are 0; Initialization μ=μ
0, carry out following iteration:
μ
t+1=(1-λ) M μ
t+ λ μ
0, wherein 0< λ <1, M is adjacency matrix
Exit iteration until stable, obtain the probability matrix μ * of steady state (SS).
Fig. 5 illustrates according to the structural drawing of another embodiment of unified address list information disposal system of the present invention.As shown in Figure 5, this embodiment comprises: graph of a relation node generation module 41, graph of a relation limit generation module 52, adjacency matrix generation module 43 and probability matrix determination module 44.Wherein, graph of a relation limit generation module 52 comprises: characteristic edge generation unit 521, for generating respectively limit between the contact attribute node user node and user and good friend's attribute node of user; Good friend limit generation unit 522, for generating limit between good friend's attribute node of good friend's user each other; Contact person limit generation unit 523, for determining contact person's user's the internodal contact person's association factor of contact attribute each other, if contact person's association factor is greater than setting threshold, between by contact person's user's each other contact attribute node, generate limit.
Traditional vector space model is given tacit consent between two users that will calculate relevant, and user's feature is simply combined, by the multidimensional characteristic of the one-dimensional space being mapped to the vector of hyperspace, and similarity between compute vector, the degree of association in two user's presentations only calculated.
Embodiment in the disclosure, in multilayer graph model, between default user, directly do not set up associated by limit, but associated by the features such as user's contact person, good friend's relation are set up, make full use of the transitivity of good friend's relation, and improve and restart type Random Walk Algorithm by contact person's association factor, finally obtain the relation between user node.
One of ordinary skill in the art will appreciate that: all or part of step that realizes said method embodiment can be by the unified address list modeling based on multilayer graph model, improvedly restart type Random Walk Algorithm and complete
Based on technique scheme, the present invention is not needing to change under the prerequisite of existing network, to unify address list abstract is multilayer graph model structure, contact person between user, the wing are chatted to the social networks such as good friend and be superposed to a unified social network diagram, the method proposes the improved type Random Walk Algorithm of restarting, and utilizes contact person's association factor to remove the noise information that may exist in contact relationship figure.Effectively digging user social networks, and then be that repeat in work promotion and derivative other services are offered help.
So far, described in detail according to unified address list information disposal route of the present invention and system.For fear of covering design of the present invention, details more known in the field are not described.Those skilled in the art, according to description above, can understand how to implement technical scheme disclosed herein completely.
May realize in many ways method and system of the present invention.For example, can realize method and system of the present invention by any combination of software, hardware, firmware or software, hardware, firmware.The said sequence that is used for the step of described method is only in order to describe, and the step of method of the present invention is not limited to above specifically described order, unless otherwise specified.In addition, in certain embodiments, can be also the program being recorded in recording medium by the invention process, these programs comprise the machine readable instructions for realizing the method according to this invention.Thereby the present invention also covers the recording medium of storing the program for carrying out the method according to this invention.
Although specific embodiments more of the present invention are had been described in detail by example, it should be appreciated by those skilled in the art, above example is only in order to describe, instead of in order to limit the scope of the invention.It should be appreciated by those skilled in the art, can without departing from the scope and spirit of the present invention, above embodiment be modified.Scope of the present invention is limited by claims.
Claims (10)
1. unify an address list information disposal route, it is characterized in that, comprising:
Build multilayer graph of a relation according to the contact person of user, user in unified address list, user's friend information, wherein each user, user's contact person, user's good friend is respectively as the node of described multilayer graph of a relation;
In described multilayer graph of a relation, between user node and described user's contact attribute node and good friend's attribute node of described user, generate respectively limit; To between good friend's attribute node of good friend's user each other, generate limit; To between contact person's user's each other contact attribute node, determine between user's contact attribute node and generate limit according to contact person's association factor;
Generate adjacency matrix according to the annexation of described multilayer graph of a relation;
Determine the probability matrix of steady state (SS) by restarting type Random Walk Algorithm based on described adjacency matrix according to initial probability matrix.
2. method according to claim 1, is characterized in that, described contact person's association factor is determined according to following formula:
Wherein, A, B are user node, and C (A ∩ B) is the quantity of the common contacts of A and B, and C (A), C (B) are A, B contact person's number separately, and N is contact person's number altogether in unified address list.
3. method according to claim 1 and 2, is characterized in that, describedly will between contact person's user's each other contact attribute node, determine that according to contact person's association factor between user's contact attribute node, generating limit comprises:
Determine contact person's user's the internodal contact person's association factor of contact attribute each other;
If described contact person's association factor is greater than setting threshold, between by contact person's user's each other contact attribute node, generate limit.
4. method according to claim 1, is characterized in that, the described annexation according to described multilayer graph of a relation generates adjacency matrix and comprises:
Generate described adjacency matrix according to the annexation of described multilayer graph of a relation, wherein, weight for the limit between user node and described user's contact attribute node and good friend's attribute node of described user is 1, being 1 for the weight on the limit between good friend's attribute node, is normalized contact person's association factor for the weight on the limit between contact attribute node;
Described adjacency matrix is normalized by row.
5. according to the method described in claim 1 or 4, it is characterized in that, the initial probability matrix of described basis determines by restarting type Random Walk Algorithm that based on described adjacency matrix the probability matrix of steady state (SS) comprises:
Generate initial matrix μ
0for N*1 rank matrix, making seed user node initial value is 1, and other node initial values are 0;
Initialization μ=μ
0;
Carry out following iteration:
μ
t+1=(1-λ) M μ
t+ λ μ
0, wherein 0< λ <1, M is adjacency matrix, t is iterations;
Exit iteration until stable, obtain the probability matrix μ * of steady state (SS).
6. method according to claim 5, is characterized in that, also comprises:
Put to the proof the tightness degree of calculating between kind of child user and other users according to the probability of described steady state (SS).
7. unify an address list information disposal system, it is characterized in that, comprising:
Graph of a relation node generation module, for building multilayer graph of a relation according to unified address list user, user's contact person, user's friend information, wherein each user, user's contact person, user's good friend is respectively as the node of described multilayer graph of a relation;
Graph of a relation limit generation module at described multilayer graph of a relation, generates respectively limit between user node and described user's contact attribute node and good friend's attribute node of described user; To between good friend's attribute node of good friend's user each other, generate limit; To between contact person's user's each other contact attribute node, determine between user's contact attribute node and generate limit according to contact person's association factor;
Adjacency matrix generation module, for generating adjacency matrix according to the annexation of described multilayer graph of a relation;
Probability matrix determination module, for determining the probability matrix of steady state (SS) by restarting type Random Walk Algorithm based on described adjacency matrix according to initial probability matrix.
8. system according to claim 7, is characterized in that, described contact person's association factor is determined according to following formula:
Wherein, A, B are user node, and C (A ∩ B) is the quantity of the common contacts of A and B, and C (A), C (B) are A, B contact person's number separately, and N is contact person's number altogether in unified address list.
9. according to the system described in claim 7 or 8, it is characterized in that, described graph of a relation limit generation module comprises:
Characteristic edge generation unit, for generating respectively limit between the contact attribute node user node and described user and good friend's attribute node of described user;
Good friend limit generation unit, for generating limit between good friend's attribute node of good friend's user each other;
Contact person limit generation unit, for determining contact person's user's the internodal contact person's association factor of contact attribute each other, if described contact person's association factor is greater than setting threshold, between by contact person's user's each other contact attribute node, generate limit.
10. system according to claim 7, it is characterized in that, described adjacency matrix generation module generates described adjacency matrix according to the annexation of described multilayer graph of a relation, wherein, weight for the limit between user node and described user's contact attribute node and good friend's attribute node of described user is 1, being 1 for the weight on the limit between good friend's attribute node, is normalized contact person's association factor for the weight on the limit between contact attribute node; Described adjacency matrix is normalized by row;
Or
Described probability matrix determination module is used for generating initial matrix μ
0, be N*1 rank matrix, making seed user node initial value is 1, other node initial values are 0; Initialization μ=μ
0, carry out following iteration:
μ
t+1=(1-λ) M μ
t+ λ μ
0, wherein 0< λ <1, M is adjacency matrix, t is iterations;
Exit iteration until stable, obtain the probability matrix μ * of steady state (SS).
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104580385A (en) * | 2014-12-16 | 2015-04-29 | 腾讯科技（深圳）有限公司 | Method and device expanding user relationship chain |
| CN105868447A (en) * | 2016-03-24 | 2016-08-17 | 南京邮电大学 | User communication behavior analysis and model simulation system based on double-layer network |
| CN107798125A (en) * | 2017-11-10 | 2018-03-13 | 携程旅游网络技术（上海）有限公司 | Access decision method, system, equipment and storage medium based on cohesion model |
| CN110489515A (en) * | 2019-08-01 | 2019-11-22 | 卫盈联信息技术（深圳）有限公司 | Method, server and the storage medium of address list retrieval |
| CN110727680A (en) * | 2019-09-25 | 2020-01-24 | 武汉奥浦信息技术有限公司 | Data association storage method, electronic equipment and storage medium |
| CN111512617A (en) * | 2017-12-22 | 2020-08-07 | 三星电子株式会社 | Device and method for recommending contact information |
| US11521111B2 (en) | 2017-12-22 | 2022-12-06 | Samsung Electronics Co., Ltd. | Device and method for recommending contact information |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102722566A (en) * | 2012-06-04 | 2012-10-10 | 上海电力学院 | Method for inquiring potential friends in social network |
| CN103077247A (en) * | 2013-01-17 | 2013-05-01 | 清华大学 | Method for building friend relationship transitive tree in social network |
-
2013
- 2013-05-28 CN CN201310202471.6A patent/CN104182422B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102722566A (en) * | 2012-06-04 | 2012-10-10 | 上海电力学院 | Method for inquiring potential friends in social network |
| CN103077247A (en) * | 2013-01-17 | 2013-05-01 | 清华大学 | Method for building friend relationship transitive tree in social network |
Non-Patent Citations (2)
| Title |
|---|
| JIAYU PAN 等: "Automatic Multimedia Cross-modal Correlation Discovery", 《ACM SIGKDD》 * |
| ZHIJUN YIN 等: "A Unified Framework for Link Recommendation Using Random Walks", 《2010 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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