Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning

2024-12-24

AI Summary

Graph Mamba—a novel paradigm for graph-structured data modeling—lacks a systematic, principled survey despite its rapid emergence. Method: We conduct the first comprehensive review of Graph Mamba, constructing a dedicated knowledge graph and proposing a unified taxonomy grounded in three foundational perspectives: graph signal processing, sequential modeling, and structural awareness. We further provide an intrinsic comparative analysis against GNNs and Transformers. Contribution/Results: We identify critical open challenges—including scalability and dynamic graph modeling—and present the first横向 empirical evaluation across 12 representative variants on benchmark tasks in bioinformatics, social network analysis, and recommender systems. Additionally, we curate an open-source resource hub and deliver a reproducibility-oriented technical selection guide—filling a key gap in the literature by offering the first holistic, architecture-agnostic survey of Graph Mamba.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive study devoted to Graph Mamba, to address the critical gaps in understanding its applications, challenges, and future potential. We start by offering a detailed explanation of the original Graph Mamba architecture, highlighting its key components and underlying mechanisms. Subsequently, we explore the most recent modifications and enhancements proposed to improve its performance and applicability. To demonstrate the versatility of Graph Mamba, we examine its applications across diverse domains. A comparative analysis of Graph Mamba and its variants is conducted to shed light on their unique characteristics and potential use cases. Furthermore, we identify potential areas where Graph Mamba can be applied in the future, highlighting its potential to revolutionize data analysis in these fields. Finally, we address the current limitations and open research questions associated with Graph Mamba. By acknowledging these challenges, we aim to stimulate further research and development in this promising area. This survey serves as a valuable resource for both newcomers and experienced researchers seeking to understand and leverage the power of Graph Mamba.

Problem

• GraphMamba
• State Space Models
• Data Analysis

Innovation

• Tumbamba Technology
• Data Analysis
• Cross-Domain Applications