Group discovery method, system and device for multi-source heterogeneous relationship network and medium

Abstract

The invention belongs to the field of graph data mining, and discloses a group discovery method, system and device for a multi-source heterogeneous relationship network, and a medium, and the method comprises the steps: obtaining interaction behavior data between any two users in the multi-source heterogeneous relationship network; obtaining an edge weight value between any two users according tothe interaction behavior data between the any two users, wherein the edge weight value is used for representing the connection closeness degree between the two users; constructing a graph structure according to the edge weight value between the any two users to obtain a multi-source heterogeneous relation graph; and performing sub-graph division on the multi-source heterogeneous relationship graphto obtain a plurality of maximum connected sub-graphs, and performing community division respectively to obtain a group discovery result of the multi-source heterogeneous relationship network. The method is low in time complexity, excellent in group discovery result, suitable for a large-scale relational network, capable of effectively reducing time resource expenditure in group discovery and improving the modularity of the discovery result, free of any priori knowledge, capable of being achieved completely through a network topology structure and high in applicability to a complex network.

Description

technical field [0001] The invention belongs to the field of graph data mining, and relates to a group discovery method, system, equipment and medium of a multi-source heterogeneous relational network. Background technique [0002] With the rapid development of Internet technology, online social networks such as QQ, Weibo, Facebook, Twitter and other social networking platforms have become an indispensable part of our lives. Users and their interactive relationships on social platforms can usually be described by complex multi-source heterogeneous relationship networks. The topological structure of interconnected nodes in these networks is organized and random, and the network data is multi-sourced and heterogeneous. , Large-scale is the main feature, the number of users reaches millions, and there are various forms of interactive information, such as follow, friends, likes, reposts, comments, etc. In recent years, due to the increasing variety of user interactions on large...

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the above-mentioned prior art. The existing method of community detection group can only obtain better results on a small-scale multi-source heterogeneous relationship network. The disadvantages of unobvious structure, small modularity, and poor group discovery results provide a group discovery method, system, device and medium for multi-source heterogeneous relational networks, which can still achieve better results in large-scale multi-source heterogeneous networks. the result of

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