2025· Neural Information Processing Systems· 0 citations· 50 references
Computer Science
TL;DR
A large-scale, universal, and joint multi-domain heterogeneous graph dataset named UniHG to facilitate heterogeneous graph representation learning and cross-domain knowledge mining and comprehensive benchmark results demonstrate that the model significantly out-performs existing methods with an accuracy improvement of 28.93%.
Abstract
Irregular data in the real world are usually organized as heterogeneous graphs consisting of multiple types of nodes and edges. However, current heterogeneous graph research confronts three fundamental challenges: i) Benchmark Deficiency, ii) Semantic Disalignment, and iii) Propagation Degradation. In this paper, we construct a large-scale, universal, and joint multi-domain heterogeneous graph dataset named UniHG to facilitate heterogeneous graph representation learning and cross-domain knowledge mining. Overall, UniHG contains 77.31 million nodes and 564 million directed edges with thousands of labels and attributes, which is currently the largest universal heterogeneous graph dataset available to the best of our knowledge. To perform effective learning and provide comprehensively benchmarks on UniHG , two key measures are taken, including i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes and edges into a common embedding space to facilitate information aggregation; ii) proposing the novel Heterogeneous Graph Decoupling (HGD) framework with a specifically designed Anisotropy Feature Propagation (AFP) module for learning effective multi-hop anisotropic propagation kernels. These two strategies enable efficient information propagation among a tremendous number of multi-attribute entities and meanwhile mine multi-attribute association adaptively through the multi-hop aggregation in large-scale heterogeneous graphs. Comprehensive benchmark results demonstrate that our model significantly out-performs existing methods with an accuracy improvement of 28.93%. And the UniHG can facilitate downstream tasks, achieving an NDCG
A combined HGT-based framework incorporating contrastive representation learning and deep clustering with multi-round training via pseudo-labels is presented, enabling the model to better deal with label scarcity, heterogeneous dependencies, and overlapping semantics in practical, complex, attributed networks.
Hamza Haddad, Hicham Attariuas, A. Younes· Edelweiss Applied Science an...· 0 citations
This paper introduces a novel task, graph textual summarization, which aims to generate natural language descriptions that capture both the semantic content and structural characteristics of graph data based on large language models (LLMs). Unlike traditional summarization tasks for text, images, or videos, summarizing graph data presents unique challenges due to its abstract and non-intuitive nature. We develop a comprehensive benchmark construction framework and construct three diverse benchmark datasets, including text-attributed graphs (TAGs), knowledge graphs (KGs), and heterogeneous attributed graphs (HAGs). To facilitate empirical studies, we deliver three representative benchmark datasets: SurveyGraphSum, consisting of 7835 citation graphs from survey papers across 10 major domains and 56 subdomains; BookGraphSum, containing 6175 knowledge graphs extracted from 186 books; and E-comGraphSum, with 912 heterogeneous graphs from real-world e-commerce networks, each representing a fraud group with 14 node types, 19 relation types, and 9 numerical attributes related to user behavior and interactions. In addition, we propose a unified evaluation framework that assesses both linguistic quality and factual consistency, providing a comprehensive approach to evaluating generated summaries for content fidelity and accuracy. By formalizing the task, releasing benchmark datasets, and proposing an evaluation framework, we aim to advance research in graph textual summarization and promote methods that produce accurate, informative, and human-readable summaries for complex graphs.
Xiaoxuan Gou, Weiguo Zheng, Hanqing Guo et al.· Proceedings of the 32nd ACM...· 0 citations
A Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner and a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs.
Mo Li, Zhaosong Zhao, Linlin Ding et al.· Annual International ACM SIG...· 0 citations
Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing methods for multi-label node classification can effectively model multiple labels, while only considering in-domain scenarios where the model needs to be trained and tested within the same graph domain, resulting in limited cross-domain generalization. Recently, Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable graph representations across diverse graph domains and downstream tasks. However, existing GFMs are built upon single-label assumption, where all nodes are arbitrarily regarded as containing only one class of semantic and embedded into a single representation. For multi-label nodes, such a representation essentially approximates multiple semantics with a single point in the representation space, inevitably leading to semantic entanglement and making simultaneous discrimination of multiple labels difficult. To address these limitations, we propose a Multi-Semantic Basis Graph Foundation Model (MSB-GFM), a framework for cross-domain multi-label node classification. Specifically, we introduce a multi-semantic basis representation learning paradigm that models each multi-label node as an adaptive composition of semantic bases, thereby enabling flexible representational capacity for modeling multiple semantics. Furthermore, we develop a semantic-structure dual-channel architecture with domain adversarial training for effective cross-domain knowledge transfer. Extensive experiments demonstrate the effectiveness of our model.
Dongxiao He, Jiayu Zhang, Jitao Zhao et al.· 0 citations
SliGFM is proposed, a graph foundation model built upon topology-aware sliding-window feature encoding and generative reconstruction that enables a smoothness-aware transformer to capture transferable relational patterns among feature tokens within each node, while the generative reconstruction objective encourages preservation of the original feature information.
