  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.
