Relevance-Aware Anomalous Users Detection in Social Network via Graph Neural Network

TL;DR

This paper introduces RAU-GNN, a relevance-aware graph neural network model that effectively detects anomalous users in social networks by leveraging multiple user relations and advanced GNN techniques.

Contribution

The paper proposes a novel RAU-GNN model that integrates GCN and GAT to improve the accuracy of anomalous user detection in large-scale social networks.

Findings

Achieves high accuracy in real-world datasets

Effectively models multiple user relations

Outperforms existing detection methods

Abstract

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.