Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning

2024-06-24

AI Summary

Systematic integration of visual information with graph structure remains underexplored in graph machine learning. Method: We introduce MM-GRAPH, the first benchmark for vision–text–structure multimodal graph learning, comprising seven real-world datasets and supporting standardized evaluation across node classification, link prediction, and other core tasks. It uniquely incorporates high-dimensional visual features as node attributes and provides an end-to-end, reproducible evaluation framework featuring GNN backbones, cross-modal alignment and fusion modules, and unified preprocessing pipelines. Contribution/Results: Empirical analysis demonstrates that incorporating visual modality substantially improves model generalization—particularly under sparse labeling and long-tailed class distributions. MM-GRAPH fills a critical gap in multimodal graph learning benchmarks and establishes a rigorous foundation for both methodological development and diagnostic analysis.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in downstream tasks remains an underexplored area. To address this critical gap, we introduce the Multimodal Graph Benchmark (MM-GRAPH), a pioneering benchmark that incorporates both visual and textual information into graph learning tasks. MM-GRAPH extends beyond existing text-attributed graph benchmarks, offering a more comprehensive evaluation framework for multimodal graph learning Our benchmark comprises seven diverse datasets of varying scales (ranging from thousands to millions of edges), designed to assess algorithms across different tasks in real-world scenarios. These datasets feature rich multimodal node attributes, including visual data, which enables a more holistic evaluation of various graph learning frameworks in complex, multimodal environments. To support advancements in this emerging field, we provide an extensive empirical study on various graph learning frameworks when presented with features from multiple modalities, particularly emphasizing the impact of visual information. This study offers valuable insights into the challenges and opportunities of integrating visual data into graph learning.

Problem

• Integrating visual and textual data in graph learning
• Assessing multimodal graph algorithms in diverse real-world scenarios
• Evaluating impact of visual information on graph learning performance

Innovation

• Integrates visual and textual data into graph learning
• Introduces Multimodal Graph Benchmark with diverse datasets
• Empirically studies impact of visual information on graphs