When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning

• URL: http://arxiv.org/abs/2410.09132v1
• Date: Fri, 11 Oct 2024 13:24:57 GMT
• Title: When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning
• Authors: Hao Yan, Chaozhuo Li, Zhigang Yu, Jun Yin, Ruochen Liu, Peiyan Zhang, Weihao Han, Mingzheng Li, Zhengxin Zeng, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Senzhang Wang,
• Abstract summary: Multimodal attributed graphs (MAGs) are prevalent in various real-world scenarios and generally contain two kinds of knowledge. Recent advancements in Pre-trained Language/Vision models (PLMs/PVMs) and Graph neural networks (GNNs) have facilitated effective learning on MAGs. We propose Multimodal Attribute Graph Benchmark (MAGB), a comprehensive and diverse collection of challenging benchmark datasets for MAGs.
• Score: 36.6581535146878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal attributed graphs (MAGs) are prevalent in various real-world scenarios and generally contain two kinds of knowledge: (a) Attribute knowledge is mainly supported by the attributes of different modalities contained in nodes (entities) themselves, such as texts and images. (b) Topology knowledge, on the other hand, is provided by the complex interactions posed between nodes. The cornerstone of MAG representation learning lies in the seamless integration of multimodal attributes and topology. Recent advancements in Pre-trained Language/Vision models (PLMs/PVMs) and Graph neural networks (GNNs) have facilitated effective learning on MAGs, garnering increased research interest. However, the absence of meaningful benchmark datasets and standardized evaluation procedures for MAG representation learning has impeded progress in this field. In this paper, we propose Multimodal Attribute Graph Benchmark (MAGB)}, a comprehensive and diverse collection of challenging benchmark datasets for MAGs. The MAGB datasets are notably large in scale and encompass a wide range of domains, spanning from e-commerce networks to social networks. In addition to the brand-new datasets, we conduct extensive benchmark experiments over MAGB with various learning paradigms, ranging from GNN-based and PLM-based methods, to explore the necessity and feasibility of integrating multimodal attributes and graph topology. In a nutshell, we provide an overview of the MAG datasets, standardized evaluation procedures, and present baseline experiments. The entire MAGB project is publicly accessible at https://github.com/sktsherlock/ATG.

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