Anomalous users detection in social network is an imperative task for security problems.  Graph Neural Networks(GNNs) have been widely applied to reveal the suspicious nodes or relevance by aggregating the neighborhood information . However, the increasing scale of users' behaviors and interactions as well as the manifold camouflage techniques have largely complicated the discrimination process, which result in the serious loss of detection accuracy . In this paper, we propose an innovate Relevance-aware Anomalous Users Detection model ( RAUD) , which extracts and learns both the explicit and implicit relevance from meta data based on heterogeneous information network (HIN) and GNNs. We employ two distinct exploring layers including a self-attention layer and a similarity calculation layer to fully consider the users' intimacy across different relationships, and a following fusion layer based on graph convolution network(GCN) to consolidate the similarity and enhance the node embedding . We evaluate our proposed model with real-world datasets and results show our approach can achieve over 90\% accuracy for user classification, far better than other comparable baselines .
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 .
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Motivated by the great power of
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have been widely applied
, many current researches adopt GNN-based detectors
R
suspicious nodes or relevance by aggregating the neighborhood information
anomalous users
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users' behaviors and interactions as well as the manifold camouflage techniques have largely complicated the discrimination process, which result in the serious loss of detection accuracy
social activities, explosive growth of users and manifold technical disguise render the user detection a difficult task
R
RAUD) , which extracts and learns both the explicit and implicit relevance from meta data based on heterogeneous information network (HIN) and GNNs. We employ two distinct exploring layers including a self-attention layer and a similarity calculation layer to fully consider the users' intimacy across different relationships, and a following fusion layer based on graph convolution network(GCN)
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
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similarity and enhance the node embedding . We evaluate our proposed model with
node embedding by aggregating the final users' embeddings. We conduct extensive experiment on
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and results show
. The experimental results show that
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over 90\% accuracy for user classification, far better than other comparable baselines
high accuracy for anomalous users detection