Cross-social-network virtual user identity alignment method based on spatio-temporal behavior data

Abstract

A cross-social-network virtual user identity alignment method based on spatio-temporal behavior data mainly comprises the following steps: 1) preprocessing spatio-temporal behavior data generated by auser on a social network to generate a user spatio-temporal behavior sequence; 2) defining and calculating the similarity of any two users between the social networks based on the spatio-temporal behavior sequence data; 3) constructing a bipartite graph taking social network users as nodes, the same social network user nodes are edgeless, and the weight of edges between different social network user nodes is equal to the user similarity; 4) calculating the maximum weight matching of the bipartite graph; and 5) generating a virtual identity alignment result based on the maximum weight matchingresult. The method can provide important theoretical basis and technical support for comprehensively analyzing the role played by the user in the social network and accurately estimating the real attribute of the user, the required data is easy to obtain in the real social network, the calculation process is easy to carry out through a distributed framework, and the calculation efficiency is improved. Virtual user identity alignment can be quickly achieved in a large-scale complex network.

Description

technical field [0001] The invention belongs to the field of social media data mining, in particular to a cross-social network virtual user identity alignment method based on spatio-temporal behavior data. Background technique [0002] With the rapid development and large-scale popularization of Internet technologies such as online social networks, data in cyberspace is increasingly characterized by multi-source heterogeneity. The virtual identities of individuals on different social networks may have different social relationships. For the purpose of protecting privacy, the user attributes filled in by individuals on social networks may not be authentic. Based on a single social network, it is difficult for technicians to Accurately assessing the real social relationships and attributes of users brings difficulties to accurate recommendation. Therefore, comprehensive analysis of individuals' virtual identities in multiple social networks is an effective solution. Among the...

AI Technical Summary

Problems solved by technology

The virtual identities of individuals on different social networks may have different social relationships. For the purpose of protecting privacy, the user attributes filled in by individuals on social networks may not be authentic. Based on a single social network, it is difficult for technicians to Accurately assessing the real social relationships and attributes of users makes accurate recommendations difficult, so comprehensive analysis of individuals' virtual identities in multiple social networks is an effective solution

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