Abstract
Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules -- node and graph memory modules -- via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the graph-level memory module is dedicated to the learning of holistic normal patterns for detecting globally abnormal graphs. The two modules are jointly optimized to detect both locally- and globally-anomalous graphs. Extensive empirical results on 16 real-world graph datasets from various domains show that i) HimNet significantly outperforms the state-of-art methods and ii) it is robust to anomaly contamination. Codes are available at: this https URL.
Abstract (translated)
Graph-level异常检测旨在识别与 graph 集合中大多数人所展现的结构异常和节点属性不同异常情况 graphs。一个主要挑战是学习正常模式在 graph 的精细和整体视图中的表现,以识别部分或整体异常的 graph。为了解决这个问题,我们提出了一种新的方法来称为Hierarchical Memory Networks(HimNet),该方法通过 graph 自动编码网络架构学习层级内存模块 - 节点和 graph 内存模块。节点级别的内存模块通过训练模型来建模节点之间的精细内部 graph 交互,以检测局部异常 graph。而 graph 级别的内存模块则专注于学习整体正常模式以检测全局异常 graph。两个模块同时优化以检测局部和全局异常的 graph。对 16 个不同领域的真实世界 graph 数据集进行广泛的实验结果表明,i) HimNet 显著优于当前最先进的方法,而 ii)它对于异常污染具有鲁棒性。代码可在 this https URL 找到。
URL
https://arxiv.org/abs/2307.00755
