Social network abnormal account detection method and system based on network representation learning

Abstract

The invention belongs to the field of social network data mining, and particularly relates to a social network abnormal account detection method and system based on network representation learning, and the method comprises the steps: building a network G (V, E, C) by using social network data; constructing an M * M-dimensional adjacent matrix of the network G (V, E, C); and constructing a representation learning joint optimization model of the network about the topological structure and the account node attribute, and the like. According to the invention, the abnormal account detection task iscombined with the network representation learning; an abnormal factor of each account node on a topological structure and an account node attribute is determined by solving the topological structureof the network account node and a low-dimensional vector representation form corresponding to the account node attribute; furthermore, the consistency characterization forms of the two account nodes in the low-dimensional space are solved, the consistency abnormal factor of each account node is calculated, and finally, the abnormal degree of each account node in the social network is evaluated bycombining the abnormal factors, so that the detection and identification of abnormal accounts are completed.

Description

technical field [0001] The invention belongs to the field of social network data mining, in particular to a method and system for detecting abnormal social network accounts based on network representation learning. Background technique [0002] With the rapid development and wide application of the third-generation Web technology, social networks have gradually become an important part of people's online life because of their high efficiency, convenience, rich content, and strong real-time performance. On the one hand, the social network is the carrier of mass media information and social information, on the other hand, it also contains a large number of users' private information and broad commercial value, which is easy to become the target of criminals. By creating false accounts or stealing normal accounts, cyber attackers publish malicious information, conduct financial transaction fraud, launch cyber attacks and other behaviors in social networks, seriously threatening...

AI Technical Summary

Problems solved by technology

However, due to the existence of "edges" in the topological structure of network data, the expression of data presents the characteristics of high-dimensional sparseness, high coupling of network account nodes, and repeated iterations of account node correlations, which brings great difficulties to the analysis and mining of network data.

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