CN109408543B - Intelligent relation network sniffing method - Google Patents
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Abstract
The invention provides an intelligent relation network sniffing method, which is characterized in that a scoring mechanism and an average index are set, each relation chain can be scored and the average index can be calculated, the scoring and the average index of the relation chain are integrated, and a path with the shortest path and the highest stability between two users is selected; by adopting an array storage form, the speed of querying array elements according to the indexes is high, a large amount of data can be stored, traversing the arrays conveniently according to the indexes can quickly query the relationship chain between users, and store the queried records, so that the data is not lost; the whole method mainly comprises the steps of searching all relation chains between users through array storage and circular traversal of an array, splitting and storing each user in the relation chains, searching relation levels between two users through a relation library and a relation type library, calculating the relation score and average index of each relation chain, and searching the shortest and most stable path between the two users.
Description
Technical Field
The invention relates to the field of information processing, in particular to an intelligent relational network sniffing method.
Background
The personal relationship discovery is a method for automatically discovering the connection relationship between people in the next generation of internet, will bring huge users and use frequency to the internet, and is an important means for embodying new technology and economic value of the internet. Finding a path between people is a basic method for personal relationship discovery, and the aim of the method is to find a path between two people who are not directly recognized, wherein a user of the path is a person, two adjacent people of the path are relationships which are mutually recognized, and the two people who are not directly recognized can be connected through the introduction of the people, so that the two people who are not directly recognized can find a way for establishing connection.
Finding personal relationships based on paths is an important method in this field, and the goal is to model social networks into graphs, represent people with users, and represent people and relationships between people with edges connecting users. In most approaches, finding people and relationships between people is achieved by finding paths in the graph that two users may have.
There are some limitations in the proposed personal relationship discovery methods: first, only the simple case of "minimum path" is considered, that is, only one path with the shortest length is searched, and the path with the shortest length may be the path with the least edges or the shortest path calculated according to the edge-to-edge weight and the number of edges. Secondly, a path scoring mechanism is lacked, when a plurality of paths are searched, paths with lower scores can be removed according to a path scoring result, and the efficiency of the system is improved, which is very important when the system is applied to a large-scale social network.
Disclosure of Invention
In view of this, the present invention provides an intelligent relationship sniffing method capable of selecting the shortest and most stable path between two users.
The technical scheme of the invention is realized as follows: the invention provides an intelligent relation network sniffing method, which comprises the following steps:
s101, each user is connected according to service data to form a relation base, the relation type of each user and adjacent users and the relation level corresponding to the relation type are determined, the relation type, the relation level and the relation description between the two users are correspondingly stored in the relation type base, the associated user in the relation base is defined as start, and the associated user is defined as end;
s102, calculating a path between two users by using a recursion principle, finding out all paths communicating two nodes, and defining an array $ result for storing a relation chain between the two nodes and a retrieval record, wherein the specific method comprises the following steps:
s201, defining a variable $ str and an array $ T1 for storing users, retrieving a relation chain with start ═ str or end ═ str in a relation base and storing the relation chain into the array $ T1, defining an associated user as $ other in the array $ T1, defining the associated user corresponding to the associated user as $ target, and defining the relation type as $ linktype;
s202, continue to execute S203 if the array $ T1 has a value, execute S204 if the $ T1 has no value;
s203, circularly traversing an array $ T1, and finding all relation chains between the relation users and the associated users; the specific method comprises the following steps:
s301, the value of the associated user at the other end of the relationship chain is taken out from the data of the current key value and is assigned to the $ other of the associated user;
s302, defining an array $ n for storing the current key value record, and constructing an associated user $ other and a relationship type $ linktype as a record array $ n for the current key value according to a format of $ n ═ n + '$ other | $ linktype';
s303, judging whether the user at the other end of the relation chain is a $ target of the target associated user, if the $ other is $ target, finding the target, and executing 304; if $ other! If $ target, S301 is executed;
s304, marking the record array $ n of the current key value as a complete relation chain and storing the record array $ result in a retrieval record array;
s204, jumping out of the current level;
s103, recording the retrieved relation chains which circularly traverse the path between the two users in an array $ result, and scoring all the relation chains between the two users, wherein the scoring method comprises the following steps: searching corresponding relation levels according to every two adjacent relation types on the relation chain, and defining the sum of the relation levels of all two adjacent users on the relation chain as the score of the relation chain;
s401, defining a relation chain stored under a current key value as $ r [ n ], splitting each user in the relation chain $ r [ n ], defining the split user as $ v after splitting, defining the relation type of two adjacent users as $ v _ linktype, storing the $ v into a record array $ n of the current key value, wherein each $ v comprises a user name and $ v _ linktype;
s402, defining a variable $ point ═ 0 for storing the relationship chain score;
s403, circularly traversing the record array $ n of the current key value, extracting the $ v _ linktype in the $ v, extracting the relation level value according to the corresponding relation between the relation type and the relation level in the relation type library, and defining the sum of the relation level values on the relation chain as the score of the relation chain;
s501, scattering the value of $ v according to the user name and the value of $ v _ linktype again to obtain the value of $ v _ linktype of the relationship chain;
s502, defining a relation level variable $ val, searching a recording value of linktype ═ v _ linktype in a relation type library, and assigning the relation level value in the relation chain to the variable $ val;
s503, defining the score on the relationship chain as $ point, accumulating the score $ point on the relationship chain according to $ point + $ val, and returning the score $ point to the array $ result;
s504, circularly executing S501-S503, scoring all relationship chains between the two users, and storing scoring results in an array $ result;
s104, in all retrieval results, sorting according to the length of the relation chain and the grade in the returned records of the complete relation chain and the grade data;
s105, defining the length of the relation chain as count ($ n), defining the average index of the relation chain as $ value, and according to the formula: and $ value ($ n), calculating the average index $ value of the relation chain, and selecting the shortest and most stable path between the two users by combining the score in the relation chain and the average index $ value of the relation chain.
Based on the above technical solution, preferably, the shorter the length of the relationship chain in S104, the higher the score of the relationship chain, the closer and firmer the relationship is represented.
On the basis of the above technical solution, preferably, the stability of a relationship chain is measured according to the average index $ value, and if the average value is high, the feasibility of the relationship chain is high.
Compared with the prior art, the intelligent relational network sniffing method has the following beneficial effects:
(1) all relation chains between two users can be found by setting a scoring mechanism and an average index, scoring is carried out on each relation chain, the average index of each relation chain is calculated, the scoring and the average index of the relation chains are integrated, and a path with the shortest path and the highest stability between the two users is selected;
(2) by adopting an array storage form, the speed of querying array elements according to the indexes is high, a large amount of data can be stored, traversing the arrays conveniently according to the indexes can quickly query the relationship chain between users, and store the queried records, so that the data is not lost;
(3) the whole method mainly comprises the steps of circularly traversing arrays through an array storage and variable assignment technology, inquiring all relation chains between users, splitting and storing each user in the relation chains, inquiring the relation level between two users by combining a relation library and a relation type library, calculating the relation grade and the average index of each relation chain, and searching the shortest and most stable path between the two users.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without any creative effort.
FIG. 1 is a flow chart of an intelligent relationship network sniffing method of the present invention;
FIG. 2 is a flowchart of the method for sniffing an intelligent relational network according to the present invention for retrieving all relationship chains between users in a relational database;
FIG. 3 is a flowchart of retrieving a relationship chain between two users in an intelligent relationship network sniffing method according to the present invention;
FIG. 4 is a flowchart of a relationship type between two adjacent users in a relationship chain between two users according to the intelligent relationship network sniffing method of the present invention;
FIG. 5 is a flowchart illustrating a scoring of a relationship chain between two users in the intelligent relationship sniffing method according to the present invention;
FIG. 6 is a partial table of a relational database constructed in an intelligent relational network sniffing method according to the present invention;
FIG. 7 is a partial table of the types of relationships constructed in the intelligent relationship sniffing method of the present invention;
FIG. 8 is a portion of a record table for retrieving relationship chains and scoring relationship chains in an intelligent relationship net sniffing method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, an intelligent relationship network sniffing method of the present invention includes the following steps:
s101, each user is connected according to service data to form a relation base, the relation type of each user and adjacent users and the relation level corresponding to the relation type are determined, the relation type, the relation level and the relation description between the two users are correspondingly stored in the relation type base, the associated user in the relation base is defined as start, and the associated user is defined as end;
s102, calculating a path between two users by using a recursion principle, finding out all paths communicating two nodes, and defining an array $ result for storing a relation chain between the two nodes and a retrieval record, as shown in FIG. 2, the specific method is as follows:
s201, defining a variable $ str and an array $ T1 for storing users, retrieving a relationship chain of start $ str or end $ str from a relational database and storing the relationship chain into the array $ T1, defining an associated user in the array $ T1 as $ other, defining the associated user corresponding to the associated user as $ target, defining a relationship type as $ linktype, taking user a to user E as an example, wherein one relationship chain between user a to user E is a-W-C-M-E, the relationship type value between user a and user W is 4, the relationship type between user W and user C is 3, the relationship type between user C and user M is 2, the relationship type between user M and user E is 1, then in the relationship chain from user a to user E, user a is defined as $ other, and user E is defined as target;
s202, continue to execute S203 if the array $ T1 has a value, execute S204 if the $ T1 has no value;
s203, circularly traversing an array $ T1, and finding all relation chains between the relation users and the associated users; as shown in fig. 3, the specific method is as follows:
s301, the value of the associated user at the other end of the relationship chain is taken out from the data of the current key value and is assigned to the $ other of the associated user;
s302, defining an array $ n for storing the current key value record, and constructing an associated user $ other and a relationship type $ linktype as the record array $ n of the current key value according to a format of $ n ═ n + '$ other | $ linktype', where a relationship chain between the user a and the user E is taken as an example, a data assembling format of $ r [ n ] is: a |0-W |4-C |3-M |2-E | 1;
s303, judging whether the user at the other end of the relation chain is a $ target of the target associated user, if the $ other is $ target, finding the target, and executing 304; if $ other! If $ target, S301 is executed;
s304, marking the record array $ n of the current key value as a complete relation chain and storing the record array $ result in a retrieval record array;
s204, jumping out of the current level;
s103, recording the retrieved relation chains which circularly traverse the path between the two users in an array $ result, and scoring all the relation chains between the two users, wherein the scoring method comprises the following steps: searching for a corresponding relationship level according to each two adjacent user types on the relationship chain, and defining the sum of the relationship levels of all two adjacent users on the relationship chain as the score of the relationship chain, as shown in fig. 4, the specific method is as follows:
s401, defining a relation chain stored under a current key value as $ r [ n ], splitting each user in the relation chain $ r [ n ], after splitting, defining the split user as $ v, defining user type values of two adjacent users as $ v _ linktype, wherein each $ v comprises a user name and $ v _ linktype, storing the $ v into a record array $ n of the current key value, taking a relation chain between a user A and a user E as an example, scoring according to [ A-W ] [ C ] [ M-E ], scattering the $ r [ n ] into the array $ n by taking '-' as a unit, and realizing the code that: when $ n is extension ("-", $ r [ n ]), the broken up a |0, W |4, C |3, M |2, and E |1 are stored in an array $ n;
s402, defining a variable $ point ═ 0 for storing the relationship chain score;
s403, circularly traversing the record array $ n of the current key value, extracting the $ v _ linktype in the $ v, extracting the relation level value according to the corresponding relation between the relation type and the relation level in the relation type library, and defining the sum of the relation level values on the relation chain as the score of the relation chain, wherein the scoring method is shown in FIG. 5;
s501, scattering the value of $ v according to the user name and the value of $ v _ linktype again to obtain the relation type value of a relation chain, $ v _ linktype, taking a relation chain between a user A and a user E as an example, scattering the value of $ v according to the unit of "|", obtaining the type value of the relation chain, and assigning the type value to the variable $ v _ linktype, wherein the method comprises the following steps:
$v_linktype＝explode("|",$v)；
$v_linktype＝$v_linktype[1]；
the method comprises the steps that W |4 is separated into W and 4, the relation type value $ v _ linktype ═ 4 between a user A and the user W is obtained, and by analogy, A |0, C |3, M |2 and E |1 are separated according to the same principle, wherein 0 of A |0 represents the relation between the user A and the user A, namely the user A retrieves the user A and the corresponding relation level is 0;
s502, defining a relation level variable $ val, searching a recording value of linktype $ v _ linktype in a relation type library, assigning the relation level value in a relation chain to the variable $ val, wherein one relation chain from a user A to a user E is A |0-W |4-C |3-M |2-E |1, the relation type is 0-4-3-2-1, and the corresponding relation level is 0-6-7-8-9;
s503, defining a score of $ point on the relationship chain, accumulating the score $ point on the relationship chain according to $ point + $ val, and returning the score $ point to an array $ result, that is, the score of a |0-W |4-C |3-M |2-E |1 relationship chain between a and the user E is 0+6+7+8+9 ═ 30;
s504, circularly executing S501-S504, scoring all relationship chains between the two users, and storing scoring results in an array $ result;
s104, in all retrieval results, sorting according to the length of the relation chain and the grade in the returned records of the complete relation chain and the grade data;
s105, defining the length of the relation chain as count ($ n), defining the average index of the relation chain as $ value, and according to the formula: and $ value ($ n), calculating the average index $ value of the relation chain, and selecting the shortest and most stable path between the two users by combining the score in the relation chain and the average index $ value of the relation chain.
Fig. 6 and 7 are a relational database and a relational type database, respectively, the relational database and the relational type database are in a one-to-one correspondence relationship, and values of the relational database and values of the relational levels are both existing data, which is not redundant, wherein in fig. 6, a star associated user is an associated user in an array, an end associated user is an associated user in an array, a linktype relational type is a relational type between two users, in fig. 7, the linktype relational type is the same as the linktype relational type of fig. 6, each linktype relational type corresponds to a relational level, the relational level is high, the more intimate the relational description in the present invention is, for example, the relational level with the relational level value of 9 indicates an immediate family, and the relational description both indicate a parent-child relationship.
FIG. 8 is a diagram illustrating a relationship chain between two users of the traversal array query and a score of the relationship chain, for example, the relationship chain has a starting point A, an end point B, the relationship chain A-W-R-M, and the relationship chain score 34.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (3)
1. An intelligent relational network sniffing method, comprising the steps of:
s101, all users are connected according to service data to form a relation base, the relation type between each user and an adjacent user and the relation level corresponding to the relation type are determined, the relation type, the relation level and the relation description between the two users are correspondingly stored in the relation type base, an associated user in the relation base is defined as start, and the associated user is defined as end;
s102, calculating a path between two users by using a recursion principle, finding out all paths communicating two nodes, and defining an array $ result for storing a relation chain between the two nodes and a retrieval record, wherein the specific method comprises the following steps:
s201, defining a variable $ str and an array $ T1 for storing users, retrieving a relation chain with start ═ str or end ═ str in a relation base and storing the relation chain into the array $ T1, defining an associated user as $ other in the array $ T1, defining the associated user corresponding to the associated user as $ target, and defining the relation type as $ linktype;
s202, continue to execute S203 if the array $ T1 has a value, execute S204 if the $ T1 has no value;
s203, circularly traversing the array $ T1, and finding out all relation chains between the relation users and the associated users; the specific method comprises the following steps:
s301, the value of the associated user at the other end of the relationship chain is taken out from the data of the current key value and is assigned to the $ other of the associated user;
s302, defining an array $ n for storing the current key value record, and constructing an associated user $ other and a relationship type $ linktype as a record array $ n for the current key value according to a format of $ n ═ n + '$ other | $ linktype';
s303, judging whether the user at the other end of the relation chain is a $ target of the target associated user, if the $ other is $ target, finding the target, and executing 304; if $ other! If $ target, S301 is executed;
s304, marking the record array $ n of the current key value as a complete relation chain and storing the record array $ result in a retrieval record array;
s204, jumping out of the current level;
s103, recording the retrieved relation chains which circularly traverse the path between the two users in an array $ result, and scoring all the relation chains between the two users, wherein the scoring method comprises the following steps: searching corresponding relation levels according to every two adjacent relation types on the relation chain, and defining the sum of the relation levels of all two adjacent users on the relation chain as the score of the relation chain;
s401, defining a relation chain stored under a current key value as $ r [ n ], splitting each user in the relation chain $ r [ n ], defining the split user as $ v after splitting, defining the relation type of two adjacent users as $ v _ linktype, storing the $ v into a record array $ n of the current key value, wherein each $ v comprises a user name and $ v _ linktype;
s402, defining a variable $ point ═ 0 for storing the relationship chain score;
s403, circularly traversing the record array $ n of the current key value, extracting the $ v _ linktype in the $ v, extracting the relation level value according to the corresponding relation between the relation type and the relation level in the relation type library, and defining the sum of the relation level values on the relation chain as the score of the relation chain;
s501, scattering the value of $ v again according to the user name and the value of $ v _ linktype, and obtaining the relation type value of the relation chain of $ v _ linktype;
s502, defining a relation level variable $ val, searching a recording value of linktype ═ v _ linktype in a relation type library, and assigning the relation level value in the relation chain to the variable $ val;
s503, defining the score on the relationship chain as $ point, accumulating the score $ point on the relationship chain according to $ point + $ val, and returning the score $ point to the array $ result;
s504, circularly executing S501-S503, scoring all relationship chains between the two users, and storing scoring results in an array $ result;
s104, sorting the returned records of the complete relationship chain and the score data according to the length of the relationship chain and the score in all the retrieval results;
s105, defining the length of the relation chain as count ($ n), defining the average index of the relation chain as $ value, and according to the formula: and $ value ($ n), calculating the average index $ value of the relation chain, and selecting the shortest and most stable path between the two users by combining the score in the relation chain and the average index $ value of the relation chain.
2. An intelligent relational network sniffing method according to claim 1, wherein: the shorter the length of the relationship chain in the S104 is, the higher the score of the relationship chain is, and the closer and firmer the relationship is represented.
3. An intelligent relational network sniffing method according to claim 1, wherein: the stability of a relation chain is measured according to the average index $ value, and the feasibility of the relation chain is high if the average value is high.
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