mlsanityData to 14 Oct 2026

COIN: Co-Cluster Infomax for Bipartite Graphs

arXiv 2206.00006 ↗First posted 31 May 2022cs.LG cs.AIcited by 14

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Abstract (arXiv)

Bipartite graphs are powerful data structures to model interactions between two types of nodes, which have been used in a variety of applications, such as recommender systems, information retrieval, and drug discovery. A fundamental challenge for bipartite graphs is how to learn informative node embeddings. Despite the success of recent self-supervised learning methods on bipartite graphs, their objectives are discriminating instance-wise positive and negative node pairs, which could contain cluster-level errors. In this paper, we introduce a novel co-cluster infomax (COIN) framework, which captures the cluster-level information by maximizing the mutual information of co-clusters. Different from previous infomax methods which estimate mutual information by neural networks, COIN could easily calculate mutual information. Besides, COIN is an end-to-end coclustering method which can be trained jointly with other objective functions and optimized via back-propagation. Furthermore, we also provide theoretical analysis for COIN. We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes. We extensively evaluate the proposed COIN framework on various benchmark datasets and tasks to demonstrate the effectiveness of COIN.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 38 of its 68 references that are in the map. First posted on arXiv 31 May 2022.

Builds on

38 of 68 references.

References · 38

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Citing papers per year: 2022: 2; 2023: 3; 2024: 7; 2025: 1; 2026: 1.

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  6. 2023STERLING: Synergistic Representation Learning on Bipartite Graphscited by 12
  7. 2023FairGen: Towards Fair Graph Generationcited by 12
  8. 2024Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputationcited by 11
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  10. 2024Cluster Aware Graph Anomaly Detectioncited by 8
  11. 2024InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Predictioncited by 8
  12. 2022ARIEL: Adversarial Graph Contrastive Learningcited by 5
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