Cluster-Guided Contrastive Graph Clustering Network

Yang, Xiu‐Wei; Liu, Yue; Zhou, Sihang; Wang, Siwei; Tu, Wenxuan; Zheng, Qun; Liu, Xinwang; Fang, Liming; Zhu, En

doi:10.1609/aaai.v37i9.26285

Cited by 48 publications

(3 citation statements)

References 33 publications

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“…The time complexity of the proposed AMGC could be discussed from the network and loss computation aspects. (Liu et al 2023), and CCGC (Yang et al 2023). In addition, we report the performance of two nongraph attribute-missing clustering methods (i.e., IMKAM (Liu 2021) and DIMVC (Xu et al 2022)) and three graph counterparts (i.e., SAT (Chen et al 2022), ITR (Tu et al 2022), and SVGA (Yoo et al 2022)).…”

Section: Computational Complexitymentioning

confidence: 99%

“…The key prerequisite for the success of current DGC methods (Bo et al 2020;Cui et al 2020;Tu et al 2021;Peng et al 2021;Gong et al 2022a,b;Liu et al 2023;Hu et al 2023;Yang et al 2022Yang et al , 2023 lies in the assumption that all samples within a graph are trustworthy and complete. However, such an assumption may not always hold in practical scenarios since it is hard to collect all information from graph data.…”

Section: Introductionmentioning

confidence: 99%

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Attribute-Missing Graph Clustering Network

Tu,

Guan,

Zhou

et al. 2024

AAAI

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Deep clustering with attribute-missing graphs, where only a subset of nodes possesses complete attributes while those of others are missing, is an important yet challenging topic in various practical applications. It has become a prevalent learning paradigm in existing studies to perform data imputation first and subsequently conduct clustering using the imputed information. However, these ``two-stage" methods disconnect the clustering and imputation processes, preventing the model from effectively learning clustering-friendly graph embedding. Furthermore, they are not tailored for clustering tasks, leading to inferior clustering results. To solve these issues, we propose a novel Attribute-Missing Graph Clustering (AMGC) method to alternately promote clustering and imputation in a unified framework, where we iteratively produce the clustering-enhanced nearest neighbor information to conduct the data imputation process and utilize the imputed information to implicitly refine the clustering distribution through model optimization. Specifically, in the imputation step, we take the learned clustering information as imputation prompts to help each attribute-missing sample gather highly correlated features within its clusters for data completion, such that the intra-class compactness can be improved. Moreover, to support reliable clustering, we maximize inter-class separability by conducting cost-efficient dual non-contrastive learning over the imputed latent features, which in turn promotes greater graph encoding capability for clustering sub-network. Extensive experiments on five datasets have verified the superiority of AMGC against competitors.

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Section: Computational Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attribute-Missing Graph Clustering Network

Tu,

Guan,

Zhou

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

“…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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PCG: A joint framework of graph collaborative filtering for bug triaging

Dai,

Li,

Xie

et al. 2024

J Software Evolu Process

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Bug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering‐based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios.

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Cluster-Guided Contrastive Graph Clustering Network

Cited by 48 publications

References 33 publications

Attribute-Missing Graph Clustering Network

Attribute-Missing Graph Clustering Network

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

PCG: A joint framework of graph collaborative filtering for bug triaging

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