# Cross-Contrastive Clustering for Multimodal Attributed Graphs with Dual Graph Filtering

arXiv: https://arxiv.org/abs/2511.20030 · HTML: https://mlsanity.com/paper/2511.20030/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 97 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://mlsanity.com/paper/2012.15828/) (2020)
- [What Should Not Be Contrastive in Contrastive Learning](https://mlsanity.com/paper/2008.05659/) (2020)
- [RedCaps: web-curated image-text data created by the people, for the people](https://mlsanity.com/paper/2111.11431/) (2021)
- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Interpreting and Unifying Graph Neural Networks with An Optimization Framework](https://mlsanity.com/paper/2101.11859/) (2021)
- [A Unified View on Graph Neural Networks as Graph Signal Denoising](https://mlsanity.com/paper/2010.01777/) (2020)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Graph Filters for Signal Processing and Machine Learning on Graphs](https://mlsanity.com/paper/2211.08854/) (2022)
- [Dink-Net: Neural Clustering on Large Graphs](https://mlsanity.com/paper/2305.18405/) (2023)
- [Large-scale representation learning from visually grounded untranscribed speech](https://mlsanity.com/paper/1909.08782/) (2019)
- [VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context](https://mlsanity.com/paper/2405.04950/) (2024)
- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [Multimodal Graph Learning for Generative Tasks](https://mlsanity.com/paper/2310.07478/) (2023)

## Built on by (most cited first, 14 of 14)

- [OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph](https://mlsanity.com/paper/2602.05576/) (2026)
- [TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution](https://mlsanity.com/paper/2603.27723/) (2026)
- [CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation](https://mlsanity.com/paper/2605.11468/) (2026)
- [LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning](https://mlsanity.com/paper/2608.24795/) (2026)
- [Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering](https://mlsanity.com/paper/2603.20829/) (2026)
- [Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs](https://mlsanity.com/paper/2606.14172/) (2026)
- [Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity](https://mlsanity.com/paper/2608.00623/) (2026)
- [LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning](https://mlsanity.com/paper/2601.21453/) (2026)
- [OptiMAG: Structure-Semantic Alignment via Unbalanced Optimal Transport](https://mlsanity.com/paper/2601.22856/) (2026)
- [GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective](https://mlsanity.com/paper/2605.15723/) (2026)
- [Multimodal Graph Negative Learning](https://mlsanity.com/paper/2606.12863/) (2026)
- [RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering](https://mlsanity.com/paper/2608.00621/) (2026)
- [Towards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach](https://mlsanity.com/paper/2609.06668/) (2026)
- [GraphSelect for Budgeted Representation Selection in Multimodal Graph Inference](https://mlsanity.com/paper/2609.33561/) (2026)

## Abstract (arXiv)

Multimodal Attributed Graphs (MMAGs) are an expressive data model for representing the complex interconnections among entities that associate attributes from multiple data modalities (text, images, etc.). Clustering over such data finds numerous practical applications in real scenarios, including social community detection, medical data analytics, etc. However, as revealed by our empirical studies, existing multi-view clustering solutions largely rely on the high correlation between attributes across various views and overlook the unique characteristics (e.g., low modality-wise correlation and intense feature-wise noise) of multimodal attributes output by large pre-trained language and vision models in MMAGs, leading to suboptimal clustering performance. Inspired by foregoing empirical observations and our theoretical analyses with graph signal processing, we propose the Dual Graph Filtering (DGF) scheme, which innovatively incorporates a feature-wise denoising component into node representation learning, thereby effectively overcoming the limitations of traditional graph filters adopted in the extant multi-view graph clustering approaches. On top of that, DGF includes a tri-cross contrastive training strategy that employs instance-level contrastive learning across modalities, neighborhoods, and communities for learning robust and discriminative node representations. Our comprehensive experiments on eight benchmark MMAG datasets exhibit that DGF is able to outperform a wide range of state-of-the-art baselines consistently and significantly in terms of clustering quality measured against ground-truth labels.