Over the last forty years, researches on anomalies have received intensified interests and the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines. Anomaly detection, which aims to identify these rare observations, is among the most vital tasks and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam, from happening. The detection task is typically solved by detecting outlying data in the features space and inherently overlooks the structural information. Graphs have been prevalently used to preserve structural information, and this raises the graph anomaly detection problem - identifying anomalous graph objects (nodes, edges, sub-graphs, and graphs). However, conventional anomaly detection techniques cannot well solve this problem because of the complexity of graph data. For the aptitudes of deep learning in breaking these limitations, graph anomaly detection with deep learning has received intensified studies recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We also highlight twelve extensive future research directions according to our survey results covering emerging problems introduced by graph data, anomaly detection and real applications.

pdf/a-comprehensive-survey-on-graph-anomaly-detection-with-deep-57msnlha3o.pdf

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.

Self-Supervised Learning of Graph Neural Networks: A Unified Review

Graph anomaly detection (GAD) suffers from heterophily — abnormal nodes are sparse so that they are connected to vast normal nodes. The current solutions upon Graph Neural Networks (GNNs) blindly smooth the representation of neiboring nodes, thus undermining the discriminative information of the anomalies. To alleviate the issue, recent studies identify and discard inter-class edges through estimating and comparing the node-level representation similarity. However, the representation of a single node can be misleading when the prediction error is high, thus hindering the performance of the edge indicator. In graph signal processing, the smoothness index is a widely adopted metric which plays the role of frequency in classical spectral analysis. Considering the ground truth Y to be a signal on graph, the smoothness index is equivalent to the value of the heterophily ratio. From this perspective, we aim to address the heterophily problem in the spectral domain. First, we point out that heterophily is positively associated with the frequency of a graph. Towards this end, we could prune inter-class edges by simply emphasizing and delineating the high-frequency components of the graph. Recall that graph Laplacian is a high-pass filter, we adopt it to measure the extent of 1-hop label changing of the center node and indicate high-frequency components. As GAD can be formulated as a semi-supervised binary classification problem, only part of the nodes are labeled. As an alternative, we use the prediction of the nodes to estimate it. Through our analysis, we show that prediction errors are less likely to affect the identification process. Extensive empirical evaluations on four benchmarks demonstrate the effectiveness of the indicator over popular homophilic, heterophilic, and tailored fraud detection methods. Our proposed indicator can effectively reduce the heterophily degree of the graph, thus boosting the overall GAD performance. Codes are open-sourced in https://github.com/blacksingular/GHRN.

/pdf/addressing-heterophily-in-graph-anomaly-detection-a-19dxhbz2.pdf

Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum

Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive setting for performance evaluation. Consequently, it has been difficult to understand which methods work well and when under a broad range of settings. To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights. (1) We benchmark the outlier detection performance of 14 methods ranging from classical matrix factorization to the latest graph neural networks. (2) Using nine real datasets, our benchmark assesses how the different detection methods respond to two major types of synthetic outliers and separately to"organic"(real non-synthetic) outliers. (3) Using an existing random graph generation technique, we produce a family of synthetically generated datasets of different graph sizes that enable us to compare the running time and memory usage of the different outlier detection algorithms. Based on our experimental results, we discuss the pros and cons of existing graph outlier detection algorithms, and we highlight opportunities for future research. Importantly, our code is freely available and meant to be easily extendable: https://github.com/pygod-team/pygod/tree/main/benchmark

pdf/bond-benchmarking-unsupervised-outlier-node-detection-on-2knx62yk.pdf

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions

GNN-based anomaly detection has recently attracted considerable attention. Existing attempts have thus far focused on jointly learning the node representations and the classifier for detecting the anomalies. Inspired by the recent advances of self-supervised learning (SSL) on graphs, we explore another possibility of decoupling the node representation learning and the classification for anomaly detection. We conduct a preliminary study to show that decoupled training using existing graph SSL schemes to represent nodes can obtain performance gains over joint training, but it may deteriorate when the behavior patterns and the label semantics become highly inconsistent. To be less biased by the inconsistency, we propose a simple yet effective graph SSL scheme, called Deep Cluster Infomax (DCI) for node representation learning, which captures the intrinsic graph properties in more concentrated feature spaces by clustering the entire graph into multiple parts. We conduct extensive experiments on four real-world datasets for anomaly detection. The results demonstrate that decoupled training equipped with a proper SSL scheme can outperform joint training in AUC. Compared with existing graph SSL schemes, DCI can help decoupled training gain more improvements.

Decoupling Representation Learning and Classification for GNN-based Anomaly Detection

We study the problem of supervised contrastive (SupCon) learning on graphs. The SupCon loss has been recently proposed for classification tasks by pulling data points in the same class closer than those of different classes. However, it could be difficult for SupCon to handle datasets with large intra-class variances and high inter-class similarities. This issue is also challenging when it couples with graph structures. To address this, we present the cluster-aware supervised contrastive learning loss (ClusterSCL1) for graph learning tasks. The main idea of ClusterSCL is to retain the structural and attribute properties of a graph in the form of nodes’ cluster distributions during supervised contrastive learning. Specifically, ClusterSCL introduces the strategy of cluster-aware data augmentation and integrates it with the SupCon loss. Extensive experiments on several widely adopted graph benchmarks demonstrate the superiority of ClusterSCL over the cross-entropy, SupCon, and other graph contrastive objectives.

ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs

太空台风是极盖区内一种新发现的大尺度亮斑状极光结构，直观表征了地磁平静期的一种堪比磁暴的太阳风能量注入现象，这更新了人们对太阳风–磁层–电离层耦合过程的认识，如何从海量星载极光数据中准确髙效识别出太空台风事件具有重要的科学意义。采用深度学习的方法，通过六种网络模型的对比，最终基于迁移学习和EfficientNetB2网络提出了一种太空台风自动识别方法。在2005－2021年美国国防气象卫星（Defense Meteorological Satellite Program，DMSP）上搭载的紫外光谱成像仪（Special Sensor Ultraviolet Spectrographic Imager，SSUSI）的观测数据中验证了该模型的有效性，识别准确率达到97.7%。研究结果表明，该方法可用于从海量星载极光观测数据中自动识别太空台风事件。 

Automatic Identification of Space Hurricane Based on Transfer Learning

Graph convolutional networks (GCNs) have been proven extremely effective in a variety of prediction tasks. The general idea is to update the embedding of a node by recursively aggregating features from the node’s neighborhood. To improve the training efficiency, modern GCNs usually sample a fixed-size set of neighbors uniformly or sample according to nodes’ importance, instead of using the full neighborhood. However, both the sampling strategies ignore the reliability of a link between the target node and its neighbor, which can be implied by the graph structure and may seriously impact the performance of GCNs. To deal with this problem, we present a Graph Convolutional Network using a Reliability-based Feature Aggregation Mechanism called GraphRFA, where we sample the neighbors for each node according to different kinds of link reliability and further aggregate feature information from different reliability-specific neighborhoods by a dual feature aggregation scheme. We also theoretically prove that our aggregation scheme is permutation invariant for the graph data, and provide two simple but effective instantiations satisfying such scheme. Experimental results demonstrate the effectiveness of GraphRFA on different datasets.

Graph Convolutional Network Using a Reliability-Based Feature Aggregation Mechanism

The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained modeling of the logical expression, or inexecutability issues due to the fine-grained modeling of disconnected classes and relations in real KBs. We propose a Fine-to-Coarse Composition framework for KBQA (FC-KBQA) to both ensure the generalization ability and executability of the logical expression. The main idea of FC-KBQA is to extract relevant fine-grained knowledge components from KB and reformulate them into middle-grained knowledge pairs for generating the final logical expressions. FC-KBQA derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than the baseline. Our code is now available at GitHub https://github. com/RUCKBReasoning/FC-KBQA.

pdf/fc-kbqa-a-fine-to-coarse-composition-framework-for-knowledge-1irro0za.pdf

FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering

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