Hard Sample Aware Network for Contrastive Deep Graph Clustering

Liu, Yue; Yang, Xiu‐Wei; Zhou, Sihang; Liu, Xinwang; Wang, Zhen; Liang, Ke; Tu, Wenxuan; Zhang, Lipeng; Duan, Jingcan; Chen, Cancan

doi:10.1609/aaai.v37i7.26071

Cited by 25 publications

(5 citation statements)

References 46 publications

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“…The latter is the reciprocal of the mean shortest path distance from all other nodes in the graph. All datasets are publicly available and have been used previously in GNN research [36].…”

Section: Suite Of Testsmentioning

confidence: 99%

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Uncertainty in GNN Learning Evaluations: A Comparison between Measures for Quantifying Randomness in GNN Community Detection

Leeney,

McConville

2024

Entropy

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(1) The enhanced capability of graph neural networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity and feature information spaces of graphs. The identification of latent communities holds practical significance in various domains, from social networks to genomics. Current real-world performance benchmarks are perplexing due to the multitude of decisions influencing GNN evaluations for this task. (2) Three metrics are compared to assess the consistency of algorithm rankings in the presence of randomness. The consistency and quality of performance between the results under a hyperparameter optimisation with the default hyperparameters is evaluated. (3) The results compare hyperparameter optimisation with default hyperparameters, revealing a significant performance loss when neglecting hyperparameter investigation. A comparison of metrics indicates that ties in ranks can substantially alter the quantification of randomness. (4) Ensuring adherence to the same evaluation criteria may result in notable differences in the reported performance of methods for this task. The W randomness coefficient, based on the Wasserstein distance, is identified as providing the most robust assessment of randomness.

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“…The latter is the reciprocal of the mean shortest path distance from all other nodes in the graph. All datasets are publicly available and have been used previously in GNN research [36].…”

Section: Suite Of Testsmentioning

confidence: 99%

“…Texas, Wisc, and Cornell are extracted from web pages from computer science departments of various universities [41]. UAT, EAT, and BAT contain airport activity data collected from the National Civil Aviation Agency, Statistical Office of the European Union and Bureau of Transportation Statistics [36].…”

Section: Suite Of Testsmentioning

confidence: 99%

Uncertainty in GNN Learning Evaluations: A Comparison between Measures for Quantifying Randomness in GNN Community Detection

Leeney,

McConville

2024

Entropy

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“…Graph neural networks (GNNs) are widely available in the real world [37,52,53] and are attracting the attention of researchers [51,56,87,89]. By treating samples as nodes and relationships between samples as edges, GNNs can easily capture the underlying relationships and rules between samples through message propagation mechanisms, which are suitable to various types of graphs [9,26,38,41,43,44].…”

Section: Temporal Graph Learningmentioning

confidence: 99%

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

Liu,

Liang,

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-modal data is the key to a better audiovisual modal. We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video. More concretely, such data is inherently multi-modal according to both audio and visual cues, which proceed in a strict chronological order. It indicates that temporal information is important in multi-modal acoustic event modeling for both intra-and inter-modal. However, existing methods deal with each modal feature independently and simply fuse them together, which neglects the mining of temporal relation and thus leads to sub-optimal performance. With this motivation, we propose a Temporal Multi-modal graph learning method for Acoustic event Classification, called TMac, by modeling such temporal information via graph learning techniques. In particular, we construct a temporal graph for each acoustic event, dividing its audio data and video data into multiple segments. Each segment can be considered as a node, and the temporal relationships between nodes can be considered as timestamps on their edges. In this case, we can smoothly capture the dynamic information in intra-modal and inter-modal. Several experiments are conducted to demonstrate

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“…In recent years, the emergence of graph neural networks (e.g. GCN [12]) has brought hope for solving the appealing problem [19][20][21][22][23]. Researchers instinctively introduce it to GAD.…”

Section: Introductionmentioning

confidence: 99%

Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning

Duan,

Zhang,

Wang

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to improve the quality of node embeddings for GAD. Despite their significant advances in detection performance, there is still a relative dearth of research on the properties of the task. GAD aims to discern the anomalies that deviate from most nodes. However, the model is prone to learn the pattern of normal samples which make up the majority of samples. Meanwhile, anomalies can be easily detected when their behaviors differ from normality. Therefore, the performance can be further improved by enhancing the ability to learn the normal pattern. To this end, we propose a normality learning-based GAD framework via multi-scale contrastive learning networks (NLGAD for abbreviation). Specifically, we first initialize the model with the contrastive networks on different scales. To provide sufficient and reliable normal nodes for normality learning, we design an effective hybrid strategy for normality selection. Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished. Eventually, extensive experiments on six benchmark graph datasets demonstrate the effectiveness of our normality learning-based scheme on GAD. Notably, the proposed algorithm improves the detection performance (up to 5.

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Hard Sample Aware Network for Contrastive Deep Graph Clustering

Cited by 25 publications

References 46 publications

Uncertainty in GNN Learning Evaluations: A Comparison between Measures for Quantifying Randomness in GNN Community Detection

Uncertainty in GNN Learning Evaluations: A Comparison between Measures for Quantifying Randomness in GNN Community Detection

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning

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