Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data
Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public datasets are often limited in scale, geogr…
Earth ObservationMOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities
Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centralized data sharing. Federated learning (FL)…
Federated LearningTumor SegmentationMM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures. While centralized learning has shown promising performa…
Graph LearningSpatialMosaic: A Multiview VLM Dataset for Partial Visibility
Recent progress in Multimodal Large Language Models (MLLMs) has enabled 3D scene understanding and spatial reasoning directly from multi-view images, without requiring explicit 3D reconstructions. Nevertheless, key chall…
Scene UnderstandingSpatial ReasoningCLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models
Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modalities and tasks, which hinders the develo…