Zombie Account Detection Based on Community Detection and Uneven Assignation PageRank

TL;DR

This paper proposes a community detection and uneven PageRank-based method to identify zombie accounts on social media, addressing computational challenges and improving detection accuracy.

Contribution

It introduces a novel approach combining Louvain community detection with uneven PageRank to efficiently detect zombie accounts in large social graphs.

Findings

Approximately 20% of accounts are zombies in the dataset.

Zombie accounts are concentrated in tier-one Chinese cities.

The method effectively decomposes large graphs for zombie detection.

Abstract

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.