Overlapping community-based malicious user detection scheme in social networks

Abstract

Currently social networks have become an important platform for social interaction and information dissemination. However, the existence of malicious social users poses a huge security threat to social networks and their information, especially it is very difficult to detect these malicious social users in overlapping communities. In this paper, we propose a malicious user detection scheme for overlapping communities-based social networks. In our scheme, we first construct a new overlapping community detection method, which is used to determine the community core and node label update order based on node influence and node relationship strength. Then we propose a malicious user detection method for overlapping communities, which is based on the changes of node attribute and node trust. In our detection method, the change trends of community attribute and message propagation influence of a node in different overlapping communities are used to determine whether the node is malicious with its specific community. Further, related experimental results show our malicious user detection scheme is effective to detect malicious users in overlapping communities.