arXiv 2211.152552023-10-01cs.LG

ARISE：基于子结构感知的属性网络图异常检测

ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness

National University of Defense Technology（国防科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Intelligent Game and Decision Laboratory（智能博弈与决策实验室）

Jingcan Duan, Bin Xiao, Siwei Wang, Haifang Zhou, Xinwang Liu

备注：: 13 pages, 8 figures, accepted by IEEE TNNLS

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AI总结：

针对现有属性网络图异常检测忽略集体拓扑异常模式的问题，提出基于子结构感知的框架ARISE，通过区域提议模块识别拓扑异常、结合图对比学习检测属性异常，在基准数据集上性能显著优于现有方法。

摘要：

近年来，属性网络图异常检测在数据挖掘和机器学习领域受到越来越多的关注。除属性异常外，图异常检测还针对表现出集体异常行为的可疑拓扑异常节点。连接紧密但不相关的节点组会在网络中形成罕见的稠密子结构。然而，现有方法忽略了识别这类集体模式可提升拓扑异常检测性能这一点。为此，我们提出了一种新的基于子结构感知的属性网络图异常检测框架（缩写为ARISE）。与以往算法不同，我们聚焦于图中的子结构来识别异常。具体而言，我们构建了一个区域提议模块，用于发现网络中的高密度子结构作为可疑区域。节点对平均相似度可视为子结构内节点的拓扑异常程度。通常而言，相似度越低，内部节点为拓扑异常的概率越高。为提炼更优的节点属性嵌入，我们进一步引入了一种图对比学习方案，该方案可同时观测属性异常。通过这种方式，ARISE能够同时检测拓扑异常和属性异常。最终，在基准数据集上开展的大量实验表明，与最先进的属性网络异常检测（ANAD）算法相比，ARISE大幅提升了检测性能（AUC最高提升7.30%，AUPRC最高提升17.46%）。

英文摘要：

Recently, graph anomaly detection on attributed networks has attracted growing attention in data mining and machine learning communities. Apart from attribute anomalies, graph anomaly detection also aims at suspicious topological-abnormal nodes that exhibit collective anomalous behavior. Closely connected uncorrelated node groups form uncommonly dense substructures in the network. However, existing methods overlook that the topology anomaly detection performance can be improved by recognizing such a collective pattern. To this end, we propose a new graph anomaly detection framework on attributed networks via substructure awareness (ARISE for abbreviation). Unlike previous algorithms, we focus on the substructures in the graph to discern abnormalities. Specifically, we establish a region proposal module to discover high-density substructures in the network as suspicious regions. The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures. Generally, the lower the similarity, the higher the probability that internal nodes are topology anomalies. To distill better embeddings of node attributes, we further introduce a graph contrastive learning scheme, which observes attribute anomalies in the meantime. In this way, ARISE can detect both topology and attribute anomalies. Ultimately, extensive experiments on benchmark datasets show that ARISE greatly improves detection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to state-of-the-art attributed networks anomaly detection (ANAD) algorithms.

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