GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs

Bhatia, Siddharth; Wang, Yiwei; Hooi, Bryan; Chakraborty, Tanmoy

doi:10.1007/978-3-030-86520-7_3

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…[79] GNN [172], [173] Graph-level Homogeneous GA [174]-[179] GNN [72], [73], [180], [181] Heterogeneous GA [182] Dynamic GA [183], [184] GNN [185] TABLE 3: Summary of anomaly detection on graphs.…”

Section: Discussionmentioning

confidence: 99%

“…Another line of works uses GE-based methods to analyze the anomaly degree of graphs [72], [73], [180], [181]. For instance, OCGTL [72] concentrates graph embeddings in the training set within a hypersphere where the distance to the hypersphere is used as the anomaly degree.…”

Section: Graph-level Anomaly Detectionmentioning

confidence: 99%

“…Auxiliary data Alignment data [245], auxiliary descriptions [216], [217], [277] Reweighting and resampling Reweighting [61], [75], [120], [125], [147], [179], [240], [242], [244], [289], resampling [36], [69], [83], [131], [134], [136], [143]- [145], [145], [148], [165], [172], [241], [243] SMOTE SMOTE [25], Mixup [65]- [67] GAN [37], [102], [110], [164], [180] Other methods Predictive data generation [74], [135], label generation [77], [167], [170] Additional constraints Condition relax constraints [139], [213]- [215], imbalance constraints [62], [112], class separation constraints …”

Section: Model Sharingmentioning

confidence: 99%

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The danger of dichloromethane in the synthesis and application of imidazole-based dyes

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Section: Discussionmentioning

confidence: 99%

Section: Graph-level Anomaly Detectionmentioning

confidence: 99%

Section: Model Sharingmentioning

confidence: 99%

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The danger of dichloromethane in the synthesis and application of imidazole-based dyes

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et al. 2022

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“…REMAD [56] characterizes and analyzes the residuals of attribute information to discover anomalies. GraphAnoGAN [9] is an anomalous snapshot ranking framework that is based on a generative adversarial network (GAN). Furthermore, GOutRank [37], Radar [34], and ConSub [45] are works that have contributed to the domain of anomaly detection as identiied through the baselines due to their ability to handle attributes of diferent data types, and the ability to consider both attribute and graph structural features.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Identification of Abnormal Nodes and Edges in Graphs

Senaratne

Christen

Williams

et al. 2022

J. Data and Information Quality

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Much of today’s data are represented as graphs, ranging from social networks to bibliographic citations. Nodes in such graphs correspond to records that generally represent entities, while edges represent relationships between these entities. Both nodes and edges in a graph can have attributes that characterize the entities and their relationships. Relationships are either explicitly known (like friends in a social network), or they are inferred using link prediction (such as two babies are siblings because they have the same mother). Any graph representing real-world data likely contains nodes and edges that are abnormal, and identifying these can be important for outlier detection in applications ranging from crime and fraud detection to viral marketing. We propose a novel approach to unsupervised detection of abnormal nodes and edges in graphs. We first characterize nodes and edges using a set of features, and then employ a one-class classifier to identify abnormal nodes and edges. We extract patterns of features from these abnormal nodes and edges, and apply clustering to identify groups of patterns with similar characteristics. We finally visualize these abnormal patterns to show co-occurrences of features and relationships between those features that mostly influence the abnormality of nodes and edges. We evaluate our approach on datasets from diverse domains, including historical birth certificates, COVID patient records, emails, books, and movies. This evaluation demonstrates that our approach is well suited to identify both abnormal nodes and edges in graphs in an unsupervised way, and it can outperform several baseline anomaly detection techniques.

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Statistical methods utilizing structural properties of time-evolving networks for event detection

Bansal,

Sharma

2024

Data Min Knowl Disc

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GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs

Cited by 5 publications

References 38 publications

The danger of dichloromethane in the synthesis and application of imidazole-based dyes

The danger of dichloromethane in the synthesis and application of imidazole-based dyes

Unsupervised Identification of Abnormal Nodes and Edges in Graphs

Statistical methods utilizing structural properties of time-evolving networks for event detection

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