  Anomalous users detection in social network is an imperative task for
security problems. Motivated by the great power of Graph Neural Networks(GNNs),
many current researches adopt GNN-based detectors to reveal the anomalous
users. However, the increasing scale of social activities, explosive growth of
users and manifold technical disguise render the user detection a difficult
task. In this paper, we propose an innovate Relevance-aware Anomalous Users
Detection model (RAU-GNN) to obtain a fine-grained detection result. RAU-GNN
first extracts multiple relations of all types of users in social network,
including both benign and anomalous users, and accordingly constructs the
multiple user relation graph. Secondly, we employ relevance-aware GNN framework
to learn the hidden features of users, and discriminate the anomalous users
after discriminating. Concretely, by integrating Graph Convolution Network(GCN)
and Graph Attention Network(GAT), we design a GCN-based relation fusion layer
to aggregate initial information from different relations, and a GAT-based
embedding layer to obtain the high-level embeddings. Lastly, we feed the
learned representations to the following GNN layer in order to consolidate the
node embedding by aggregating the final users' embeddings. We conduct extensive
experiment on real-world datasets. The experimental results show that our
approach can achieve high accuracy for anomalous users detection.
