Zombie Account Detection Based on Community Detection and Uneven Assignation PageRank
This research provides a method for social media platforms to identify and mitigate the negative impact of zombie accounts on public opinion, addressing the computational challenges of large graphs.
The paper addresses the problem of detecting zombie accounts in social media by first decomposing the social graph into 1,002 subgraphs using the Louvain community detection algorithm, achieving a modularity of 0.58. Subsequently, an uneven assignation PageRank algorithm is applied to each subgraph to identify zombie accounts, revealing that approximately 20% of accounts in the dataset are zombies, predominantly located in major Chinese cities.
In the social media, there are a large amount of potential zombie accounts which may has negative impact on the public opinion. In tradition, PageRank algorithm is used to detect zombie accounts. However, problems such as it requires a large RAM to store adjacent matrix or adjacent list and the value of importance may approximately to zero for large graph exist. To solve the first problem, since the structure of social media makes the graph divisible, we conducted a community detection algorithm Louvain to decompose the whole graph into 1,002 subgraphs. The modularity of 0.58 shows the result is effective. To solve the second problem, we performed the uneven assignation PageRank algorithm to calculate the importance of node in each community. Then, a threshold is set to distinguish the zombie account and normal accounts. The result shows that about 20% accounts in the dataset are zombie accounts and they center in tier-one cities in China such as Beijing, Shanghai, and Guangzhou. In the future, a classification algorithm with semi-supervised learning can be used to detect zombie accounts.