CN112989272A - Community discovery algorithm based on local path - Google Patents
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Abstract
The invention belongs to the technical field of big data processing, and particularly relates to a community discovery algorithm based on a local path, which aims to overcome the defects caused by the overall situation of the traditional community discovery algorithm.
Description
Technical Field
The invention belongs to the technical field of big data processing, and particularly relates to a community discovery algorithm based on a local path.
Background
Many practical networks are found to have a community structure, that is, the whole network is composed of several communities, the connections between communities are relatively sparse, and the connections inside communities are relatively dense. The community discovery is to analyze the modularized community structure from the complex network by utilizing the information contained in the graph topological structure, and the deep research of the problem is beneficial to researching the modules, functions and evolution of the whole network in a divide-and-conquer mode, so that the organization principle, the topological structure and the dynamic characteristic of the complex system are more accurately understood, and the method has very important significance.
The bank masters that data resources are more, the number of nodes is hundreds of millions, and in the whole network, if community discovery is carried out from the global perspective, the calculation amount demand is larger, the community modularity is not easy to control, the modularity is set to be higher, the number of acquired communities is more, the modularity is lower, and money laundering communities possibly contain more normal users with relations.
The traditional community discovery algorithm generally performs community division on a global network, and with the expansion of network scale, the disadvantages of the traditional algorithm appear, and the main expression is as follows: the model has low performance and low efficiency, and cannot meet the requirements of people, so that the research of a community discovery algorithm based on a local path is necessary.
Disclosure of Invention
Aiming at the defects and problems of the existing equipment, the invention provides a community discovery algorithm based on a local path, which effectively solves the problems of low model performance and low efficiency of the existing equipment.
The technical scheme adopted by the invention for solving the technical problems is as follows: a local path-based community discovery algorithm, comprising the steps of:
step 1, data acquisition
Acquiring a designated node from a database, and acquiring a related node which is directly related or indirectly related to the designated node at one level;
step 2: data pre-processing
Acquiring an original relation between a designated node and an associated node, and removing duplication of the original relation based on weak duplication removal logic of ductility and data equality after time to obtain an effective relation between the designated node and the associated node; then appointing an effective relation to determine the number of times of the round trip and the total value of all round trips in a time period as an edge index;
step 3, calculating local similarity coefficient
If all the times of the node in the determined time period exceed a preset time y and the total value exceeds a preset value x, determining that the relation of the node and the associated node is a, otherwise, determining that the relation of the node and the associated node is b, and setting a + b as 1; then obtaining an adjacency matrix A of the node network according to the values of a and bij；
And (3) local similarity coefficient calculation is carried out according to a third-order adjacency matrix:
in which λ is the maximum eigenvalue, AijIs a contiguous matrix, SijIs a local similarity coefficient;
step 4, comparing the local similarity coefficient with a threshold value
Comparing the local similarity coefficient obtained in the step (3) with a set threshold, if the local similarity coefficient is larger than the set threshold, adding the node network into the community, otherwise, not adding the node network into the community; carrying out community expansion through local coefficient judgment until all nodes are divided;
step 5, obtaining the rationality of the community by adopting local modularity inspection
Defining the node set of the community as V, V*Is a neighboring node of V, V*Is a contiguous matrix of
Positioning local modularity
Wherein δ (c)i，cj) If i, j are both in V, then 1, otherwise 0; m is*Identifying a number of edges within the adjacency matrix;
if the modularity is larger than a preset modularity threshold value, the rationality of the community meets the requirement, otherwise, the next step is carried out;
step 6, processing communities with substandard rationality
Sequencing according to the degree of the nodes in the community, carrying out time weak association judgment on the node with the minimum degree, if the weak association judgment meets the requirement, keeping the node in the community, removing the node from the sequencing, and carrying out weak association judgment on the node with the minimum degree again; and if the weak association judgment does not meet the requirement, removing the node from the community, and recalculating and comparing the modularity according to the method in the step 5 until the modularity is greater than the threshold of the modularity.
Further, in step 1, the designated node is obtained by big data screening.
Further, in step 2, the determined time period is one month or one year.
Further, in step 3, a is 1 and b is 0.
The invention has the beneficial effects that: according to the invention, the original relation is subjected to duplicate removal based on the weak duplicate removal logic of time delay and data equality, the accuracy of data is improved, the number of times of the round trip and the total data value are specified as side indexes, then the local similarity coefficient is calculated by utilizing a three-order adjacency matrix, and the local similarity coefficient is compared with a set threshold value to be used as the basis for judging whether the node is added into the community, the whole network information is not required to be acquired, the known characteristic node is used as a break, and the multi-dimensional index proportion of the adjacent node is calculated one by one to be used as a classification basis.
Meanwhile, the algorithm community algorithm can be judged according to the newly added nodes, the whole community is automatically optimized according to modularity, and the quality of members in the community is ensured.
Therefore, the invention provides a brand-new local community discovery algorithm, which aims to overcome the defects brought by the overall situation of the traditional community discovery algorithm, gradually increases in a spreading mode by starting from local designated nodes, is particularly practical and effective for carrying out community division on a larger network structure, has high efficiency in the discovery process and is equivalent to the overall community discovery in effect, finds related groups rapidly through input nodes, adds time weak association in group judgment to carry out group screening, can effectively reduce the group misjudgment rate, improves the local community effect, can reasonably delete non-community members with strong correlation in the community, and enhances the community discovery accuracy.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
Example 1: the invention provides a community discovery method which can be used for carrying out community discovery in a local range from part of given nodes, quickly acquiring related communities according to mastered nodes (the nodes are not necessarily central nodes), and adding gradually mastered non-community nodes into the community to quickly update the community.
The following describes the present invention in detail with reference to an example, specifically as shown in fig. 1, the embodiment provides a community discovery algorithm based on a local path, and takes an example of a bank discovering an abnormal transaction as an example, and specifically includes the following steps:
step 1, data acquisition
Acquiring a designated node from a database, and acquiring a related node which is directly related or indirectly related to the designated node at one level; the designated node is node detailed transaction flow data obtained from a big data platform through screening.
Step 2: data pre-processing
Acquiring an original relation between a designated node and an associated node, and removing duplication of the original relation based on weak duplication removal logic of ductility and data equality after time to obtain an effective relation between the designated node and the associated node; for example, there is trade of A- > B, if the data is complete, there is trade of B < -A, if the data is incomplete, even if A- > B has many trades, there is not necessarily many trades of B < -A, and the time does not have correspondence, for the form is converted into A- > B uniformly, and leave the conversion mark, carry on the duplicate removal to the data through time and conversion mark; then appointing an effective relation to determine the number of times of the round trip and the total value of all round trips in a time period as an edge index; the determined time may be one month, one quarter, or one year.
Step 3, calculating local similarity coefficient
If all the times of the node in the determined time period exceed a preset time y and the total value exceeds a preset value x, determining that the relation of the node and the associated node is a, otherwise, determining that the relation of the node and the associated node is b, and setting a + b as 1; in the example, a is 1, b is 0, and then the adjacency matrix A of the node network is obtained according to the values of a and bij；
And (3) local similarity coefficient calculation is carried out according to the adjacency matrix:
in the formula (1), lambda is the maximum characteristic value, AijIs a contiguous matrix, SijIs a local similarity coefficient;
local similarity coefficient in this example:
a in the formula (2)ijIs a contiguous matrix, k is a coefficient whose value decreases with path length; in the actual use process, S is calculatedijThe order of the information obtained when the shortest path is taken has no repeated information and the accuracy is also highPreferably. When S isijIs not equal to the average shortest path (e.g. if there is a direct relationship between ij, then S is usedij＝k1AijCan better show the local similarity coefficient, adopt
Repeated path calculations are performed on it, which results in a large local similarity coefficient). Too high an order may create redundancy of information, resulting in six degrees of separation, and therefore, in this example, three S terms are usedijTo calculate a local similarity coefficient.
Step 4, comparing the local similarity coefficient with a threshold value
Comparing the local similarity coefficient obtained in the step (3) with a set threshold, if the local similarity coefficient is larger than the set threshold, adding the node network into the community, otherwise, not adding the node network into the community; carrying out community expansion through local coefficient judgment until all nodes are divided;
step 5, obtaining the rationality of the community by adopting local modularity inspection
The node set defining the community is V, all the adjacent nodes of the nodes are added into the set to form a new set V, namely V is the adjacent node of V, and the adjacent matrix of V is
And positioning local modularity, similar to global modularity, and measuring the quality of a community by using the proportion of elements in a node set V which all belong to the node set V:
δ (c) in equation 4i，cj) If i, j are both in V, then it is 1, otherwise it is 0; m is*The number of edges within the adjacency matrix is identified.
If the modularity is greater than a preset modularity threshold value, the rationality of the community meets the requirement, otherwise, the next step is carried out; setting a threshold value as a community expansion cycle ending condition, ending the community discovery algorithm if the threshold value is met, and otherwise, performing chain time weak association calculation to perform community attenuation.
Step 6, processing communities with substandard rationality
Sequencing according to the degree of the nodes in the community, and performing time weak association judgment on the node with the minimum degree, wherein the time weak association judgment is based on the following steps: weak association of chain time: if the chain is A- > B- > C, then A- > B occurs before B- > C.
If the weak association judgment meets the requirement, the node is kept in the community, the node is removed from the sorting, and the weak association judgment is carried out on the node with the minimum degree again; and if the weak association judgment does not meet the requirement, removing the node from the community, and recalculating and comparing the modularity according to the method in the step 5 until the modularity is greater than the threshold of the modularity.
In the step, the nodes are removed by utilizing the chain time weak association, if the chain time weak association does not exist in the nodes, the data is subjected to edge deletion operation, the local correlation of the correlation nodes with the deletion relation is recalculated until the local modularity is met, the accuracy of the data in the community is further ensured, and the group misjudgment rate is effectively reduced.
The method can analyze the mass transaction data of banks, particularly, during the analysis of the flowing funds, find abnormal transaction users, calculate the local modularity of the community through a time weak related chain, and exclude the related users normally trading with a group organization mechanism.
Therefore, the method starts from local designated nodes, gradually increases in a spreading mode, is very practical and effective for carrying out community division on a larger network structure, is efficient in discovery process and equivalent to global community discovery in effect, finds relevant communities rapidly through the input nodes, adds time weak association to community judgment for community screening, can effectively reduce the misjudgment rate of communities, improves the effect of local communities, and can reasonably delete non-community members with strong correlation in the communities to enhance the accuracy of community discovery.
Example 2: this example is substantially the same as example 1, except that: in the embodiment, for the defect of the existing similarity coefficient calculation, the replacement supplement of the triangular similarity coefficient is added.
The concrete scheme is as follows
Firstly, according to step 3, a similarity coefficient based on the adjacency matrix is obtained
The core idea of obtaining the similarity coefficient based on the adjacency matrix is that the weighted sum of paths with different lengths between i and j is adopted, but for the non-high-density convergent-type community, the paths with different lengths are single (chain community), and S is obtainedijThe smaller value will destroy the integrity of the community.
Therefore, the similarity coefficient is supplemented as follows:
Similarij＝max(Sij,Cj)
wherein C isjAnd calculating the clustering coefficient of the j node for the j node based on the triangular clustering coefficient.
Wherein v isiIs a vertex i, vjIs a vertex j, eijIs the edge of vertices i and j, kjIndicating the number of nodes directly connected to node j.
Using a SimilarijThe effect is far more remarkable than S in the aspect of facing chain type trading structure communityijIn the non-chained transaction Structure Community, due to CjIn a non-chained transaction structure S only takes into account neighboring nodes without taking into account relationships between indirect nodesij>Cj。
Replacing the calculation of the Similar coefficients in the step 3 with SimilarijThe community may not be overly pruned in the face of a chain trading structure community.
Example 3: this example is substantially the same as example 1, except that: in the embodiment, for the defect of the existing similarity coefficient calculation, the triangular similarity coefficient is added for loss supplement.
The concrete scheme is as follows
Firstly, according to step 3, a similarity coefficient based on the adjacency matrix is obtained
The core idea of similarity coefficient based on the adjacency matrix is the weighted sum of paths with different lengths between i and j, only the first three terms are adopted, and when i and j have three-term internal relation (i.e. i- > x- > j, wherein x is any node), the length path number weighting when the distance between i and j exceeds 3 is not calculated, and the information is lost to a certain extent.
Therefore, the similarity coefficient is supplemented as follows:
Similarij＝a*Sij+b*Jaccardij
wherein JaccardijThe similarity index is named by Jaccard and is defined in a mode that the common neighbor number of two vertexes is the sum of all the neighbor numbers of the vertexes;
wherein v isiIs a set of nodes directly related to vertex i, where vjUsing Simiar for the set of nodes directly related to vertex jijReduce the information loss based on the adjacency matrix to a certain extentAnd (5) problems are solved.
Wherein a and b are weights, and in this example, both values a and b are 0.5, i.e., Similarij＝0.5*Sij+0.5*Jaccardij。
Claims (4)
1. A community discovery algorithm based on local paths, characterized by: the method comprises the following steps:
step 1, data acquisition
Acquiring a designated node from a database, and acquiring a related node which is directly related or indirectly related to the designated node at one level;
step 2: data pre-processing
Acquiring an original relation between a designated node and an associated node, and removing duplication of the original relation based on weak duplication removal logic of ductility and data equality after time to obtain an effective relation between the designated node and the associated node; then appointing an effective relation to determine the number of times of the round trip and the total value of all round trips in a time period as an edge index;
step 3, calculating local similarity coefficient
If all the times of the node in the determined time period exceed a preset time y and the total value exceeds a preset value x, determining that the relation of the node and the associated node is a, otherwise, b and a + b = 1; then obtaining the adjacency matrix of the node network according to the values of a and b
；
And (3) local similarity coefficient calculation is carried out according to a third-order adjacency matrix:
in which x is the maximum characteristic value,
in the form of a contiguous matrix, the matrix,
is a local similarity coefficient;
step 4, comparing the local similarity coefficient with a threshold value
Comparing the local similarity coefficient obtained in the step (3) with a set threshold, if the local similarity coefficient is larger than the set threshold, adding the node network into the community, otherwise, not adding the node network into the community; carrying out community expansion through local coefficient judgment until all nodes are divided;
step 5, obtaining the rationality of the community by adopting local modularity inspection
The set of nodes defining a community is V,
a neighboring node that is a node of V,
is a contiguous matrix of
Positioning local modularity
Wherein
If i, j are both in V, then 1, otherwise 0;
if the modularity is larger than a preset modularity threshold value, the rationality of the community meets the requirement, otherwise, the next step is carried out;
step 6, processing communities with substandard rationality
Sequencing according to the degree of the nodes in the community, carrying out time weak association judgment on the node with the minimum degree, if the weak association judgment meets the requirement, keeping the node in the community, removing the node from the sequencing, and carrying out weak association judgment on the node with the minimum degree again; and if the weak association judgment does not meet the requirement, removing the node from the community, and recalculating and comparing the modularity according to the method in the step 5 until the modularity is greater than the threshold of the modularity.
2. The local path-based community discovery algorithm of claim 1, wherein: in step 1, the designated node is obtained by big data screening.
3. The local path-based community discovery algorithm of claim 1, wherein: in step 2, the determined time period is one month or one year.
4. The local path-based community discovery algorithm of claim 1, wherein: in step 3, a is 1 and b is 0.
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