Graph Learning

2025-07-07

AI Summary

Graph learning has demonstrated advantages in modeling non-Euclidean relational structures for applications such as drug discovery, fraud detection, and recommender systems; however, it faces fundamental challenges—including poor scalability, weak generalization, difficulty in modeling heterogeneous dynamic graphs, limited interpretability, and low trustworthiness. To address these, this project proposes a unified learning framework for dynamic heterogeneous graphs, integrating graph neural networks, temporal modeling, multimodal fusion, graph generation models, explainability techniques, and privacy-preserving mechanisms—while systematically incorporating generative AI and responsible AI paradigms. The outcomes span six cutting-edge directions: scalable, temporal, multimodal, generative, interpretable, and responsible graph learning. This work establishes a technically rigorous and practically actionable framework, offering both theoretical depth and methodological guidance for advancing graph learning in complex real-world scenarios.

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Graph learning is significant due to its ability to model complex, non-Euclidean relationships that traditional machine learning struggles to capture, thus better supporting real-world applications ranging from drug discovery and fraud detection to recommender systems and scientific reasoning. However, challenges like scalability, generalization, heterogeneity, interpretability, and trustworthiness must be addressed to unlock its full potential. This survey provides a comprehensive introduction to graph learning, focusing on key dimensions including scalable, temporal, multimodal, generative, explainable, and responsible graph learning. We review state-of-the-art techniques for efficiently handling large-scale graphs, capturing dynamic temporal dependencies, integrating heterogeneous data modalities, generating novel graph samples, and enhancing interpretability to foster trust and transparency. We also explore ethical considerations, such as privacy and fairness, to ensure responsible deployment of graph learning models. Additionally, we identify and discuss emerging topics, highlighting recent integration of graph learning and other AI paradigms and offering insights into future directions. This survey serves as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of graph learning.

Problem

• Addressing scalability and generalization in graph learning models
• Enhancing interpretability and trustworthiness of graph neural networks
• Integrating heterogeneous data modalities for multimodal graph learning

Innovation

• Scalable graph architectures for large-scale data
• Dynamic temporal modeling for evolving graphs
• Multimodal learning integrating heterogeneous data