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Community discovery method and device based on entity similarity in knowledge graph

A technology of entity similarity and knowledge graph, applied in the field of community discovery method and device based on entity similarity in knowledge graph, can solve the problems of time-consuming complexity, modularity algorithm deviation, enlarged search space, etc. Second iteration time and total number of iterations, high accuracy and accurate results

Active Publication Date: 2018-12-07
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This makes the search space of the algorithm larger during iterations, thus consuming more time complexity
The modularity method is to divide the community by calculating the concept of edge betweenness, and the edge betweenness also depends on the connectivity of nodes. As mentioned above, since the users of Unicom may not be similar, this makes the modularity algorithm also have certain deviation

Method used

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  • Community discovery method and device based on entity similarity in knowledge graph
  • Community discovery method and device based on entity similarity in knowledge graph
  • Community discovery method and device based on entity similarity in knowledge graph

Examples

Experimental program
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Embodiment 1

[0041] Such as figure 1 As shown, the community discovery method based on entity similarity in the knowledge map provided by the embodiment of the present invention may include the following steps:

[0042] Step S101: use the knowledge graph to store social network data, and calculate the Jaccard distance to obtain a similarity matrix;

[0043] Step S102: Calculate a set of similar nodes in the knowledge graph according to the similarity matrix;

[0044] Step S103: Carry out iterative label propagation according to the set of similar nodes, and determine the final community label of the node according to the iterated label list of each node for community discovery.

[0045] The present invention uses the similarity based on Jaccard distance as the core. Since there are a large number of missing data in the community network, using Euclidean distance or cosine similarity will substitute missing attributes into the calculation, which will increase the similarity between nodes w...

Embodiment 2

[0047]On the basis of the community discovery method based on the entity similarity in the knowledge graph provided in Embodiment 1, the process of calculating the similar node set in the knowledge graph according to the similarity matrix in step S102 can be specifically implemented in the following manner :

[0048] (1) Receive a preset radius r and a similarity threshold s;

[0049] (2) For each node in the knowledge graph, search for nodes whose similarity with the current node is greater than the similarity threshold s within the preset radius r of the current node, and join the similar node set of the current node.

[0050] The invention replaces the set of connected nodes with a set of similar nodes, reduces the search consumption of the algorithm in the iterative process, thereby reduces the single iteration time and the total number of iterations, and finally reduces the total complexity of the algorithm.

Embodiment 3

[0052] On the basis of the community discovery method based on the entity similarity in the knowledge graph provided in the second embodiment, the process of iterative label propagation according to the similar node set described in step S103 can be specifically implemented in the following manner:

[0053] (1) Initialize the label list for each node in the knowledge graph, and initialize a unique label in the label list of each node, and the weight is 1;

[0054] (2) Set the initial value of the current number of iterations to 0, judge whether the current number of iterations is less than the preset number of iterations t, if so, perform label propagation operations on each node in the knowledge map in turn, otherwise end the iteration;

[0055] When the label propagation operation is performed on each node in the knowledge graph in turn, for the current node, the current node is used as the listener, and all nodes in the similar node set of the current node are used as the di...

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Abstract

The invention relates to the technical field of data processing, and provides a community discovery method and device based on entity similarity in a knowledge graph. The method comprises the following steps: storing a social network data by using the knowledge graph, and calculating a Jacquard distance to obtain a similarity matrix; calculating a similar node set in the knowledge graph accordingto the similarity matrix; and performing iterative label propagation according to the similar node set, and determining a final community label of the node according to a label list of each node afterthe iteration to perform community discovery. A community network is stored by using the knowledge graph, thereby avoiding the storage of structure of missing data, and on the basis, the Jacquard distance is used as the basis for calculating the similarity, so that the accuracy is higher.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a community discovery method and device based on entity similarity in a knowledge map. Background technique [0002] In a huge social network, there are usually a large number of user entities and related events, places, etc., and different user entities often have certain similarities in behavior, information, etc., and users usually have no way to accurately And efficiently search for these users who are similar to themselves. Therefore, mining the similarity of user entities in social networks and analyzing the information and behaviors of similar users can achieve the purpose of recommending friends and personalized behaviors to users. [0003] On the other hand, similar user groups in social networks are often small groups with similar hobbies and personalities, which can form communities in social networks, make community recommendations for users, and help users m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q50/00
CPCG06Q50/01
Inventor 王宏志邹开发万晓珑杨东华
Owner HARBIN INST OF TECH
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