Group discovery method and system for attribute network

Abstract

The invention belongs to the field of network data mining, and discloses a group discovery method and system for an attribute network, which are used for accurately identifying a potential group structure in the attribute network, and comprises the following steps: obtaining user interaction behavior data of the attribute network; modeling an attribute network topology and a node attribute set by preprocessing the attribute network data; positioning a potential clustering center node based on the node degree centrality measurement and the relative distance between the nodes; converting the network adjacency matrix into a similar matrix according to the topological structure information, and synthesizing a node attribute matrix at the same time; carrying out deep fusion on structure information and node attributes at the same time by using a multilayer graph convolution model, and automatically identifying a complete group structure; and finally, evaluating a group discovery result. The method and system can face large-scale attribute network data, reveals the group structure under low time complexity, is high in universality to complex networks, and has high application value.

Description

technical field [0001] The invention belongs to the field of graph data mining. Specifically, it relates to a method and system for group discovery of an attribute network. Background technique [0002] With the continuous development of information technology and Internet technology, the connection and interaction between people and the environment have become common and complex, thus forming various complex systems. These complex systems can usually be abstractly described by complex networks, such as online social networks, mobile communication networks, and so on. Complex networks involve the intersection of physics, biology, social science, systems science, network science and other fields, and have gradually become a powerful tool for solving complex problems. Analysis and many other fields have a wide range of applications. The network topologies formed by the interconnected individuals in these complex network systems are random and self-organized, and exhibit obv...

AI Technical Summary

Problems solved by technology

However, the above-mentioned methods usually fail to utilize this information, resulting in lower precision and accuracy of group discovery.

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