Social Relationships and Temp-Spatial Behaviors Based Community Discovery to Improve Cyber Security Practices

Cyber security significantly relies on the dynamic communities in social networks. The location-based social network (LBSN) is a new type of social system that has sprung up recently that. It turns traditional social networks into heterogeneous networks by incorporating location information, which is used as the medium between the real world and the online social networks, thus bringing new challenges to the community discovery problems. This paper proposes a LBSN homogeneous network model (LSHNM) based on the user social relations and temp-spatial behaviors to calculate the user similarity relations in multi-dimensional features and construct LBSN isomorphism network topology, which can be used to improve cyber security practices. After that non-negative matrix decomposition (NMF) is used to find communities from above isomorphism network topology. The experimental results show that the LSHNM can find more satisfactory community structures.

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