Abstract: Social media systems provide a convenient platform for sharing,communication and collaboration.When people enjoy the openness and convenience of social media,there may be many malicious acts,such as bullying,terrorist attacks and fraudulent information dissemination.Therefore,it is very important to be able to detect these anomalous activities as accurately and early as possible to prevent disasters and attacks.The success of online social networks (OSN) in recent years,such as Twitter,Facebook,Google+,LinkedIn,has made them targests of attacker’s goal due to their rich profit resources.The openness of social networks makes them particularly vulnerable to unusual account attacks.Existing classification models mostly use method that first assign weights to the edges of the graph,iteratively propagate the reputation scores of the nodes in the weighted graph,and use the final posterior scores to classify the nodes.One of the important tasks is the settingof edge weight.This parameter will directly affect the accuracy of the test results.Based on the detection task of fake account in social media,this paper analyzed the global structure based on social graph,and improves the algorithm of edge weight in the paired Markov random field model,so that it can adaptively optimize in the iterative process.GANG+LW,GANG+LOGW,and GANG+PLOGW algorithms with higher accuracy were proposed.These three algorithms used three different methods to improve the algorithm of edge weight.Experiments show that the proposed method can obtain more accurate fake account detection results than the basic paired Markov random field model,in which GANG+PLOGW got the best results in the three algorithms.The result proves that this improved model can solve the problem more effectively when detecting fake accounts in social networks.

Key words: Fake account detection, Markov random field, Social media, Sybil attack