# Dual Information Enhanced Multi-view Attributed Graph Clustering

arXiv: https://arxiv.org/abs/2211.14987 · HTML: https://mlsanity.com/paper/2211.14987/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 93 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Data Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2006.06830/) (2020)
- [Adversarial Graph Augmentation to Improve Graph Contrastive Learning](https://mlsanity.com/paper/2106.05819/) (2021)
- [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020)
- [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://mlsanity.com/paper/1906.06532/) (2019)
- [Structural Deep Clustering Network](https://mlsanity.com/paper/2002.01633/) (2020)
- [Graph Information Bottleneck for Subgraph Recognition](https://mlsanity.com/paper/2010.05563/) (2020)
- [Multi-view Contrastive Graph Clustering](https://mlsanity.com/paper/2110.11842/) (2021)
- [Essential Tensor Learning for Multi-view Spectral Clustering](https://mlsanity.com/paper/1807.03602/) (2018)
- [On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization](https://mlsanity.com/paper/1610.07126/) (2016)
- [Principled Multilayer Network Embedding](https://mlsanity.com/paper/1709.03551/) (2017)
- [Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency](https://mlsanity.com/paper/2008.10208/) (2020)
- [Consistent Multiple Graph Embedding for Multi-View Clustering](https://mlsanity.com/paper/2105.04880/) (2021)
- [Multi-view Subspace Clustering via Partition Fusion](https://mlsanity.com/paper/1912.01201/) (2019)
- [Multilayer Graph Contrastive Clustering Network](https://mlsanity.com/paper/2112.14021/) (2021)
- [High-order Multi-view Clustering for Generic Data](https://mlsanity.com/paper/2209.10838/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning with Dual-level Noisy Correspondence for Multi-modal Entity Alignment](https://mlsanity.com/paper/2510.18240/) (2025)
- [MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network](https://mlsanity.com/paper/2511.05554/) (2025)

## Abstract (arXiv)

Multi-view attributed graph clustering is an important approach to partition multi-view data based on the attribute feature and adjacent matrices from different views. Some attempts have been made in utilizing Graph Neural Network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel Dual Information enhanced multi-view Attributed Graph Clustering (DIAGC) method is proposed in this paper. Specifically, the proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables GCN to capture the more essential low-level representations. Besides, the Mutual Information Maximization (MIM) module maximizes the agreement between the latent high-level representation and low-level ones, and enables the high-level representation to satisfy the desired clustering structure with the help of the Self-supervised Clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.