Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems

2025-02-11

AI Summary

Existing graph learning methods are predominantly task-specific, lacking general-purpose modeling capabilities. To address this, we propose UGN—the first universal graph neural network framework—capable of unifying six heterogeneous graph tasks: link prediction, node classification, community detection, graph translation, knowledge graph completion, and graph clustering. UGN introduces a novel cross-task deep unification paradigm, featuring hyper-node coarsening and the Mean Target Connectivity Matrix (MTCM) representation to jointly ensure scalability and generalization. It synergistically integrates GCN and Conv2D layers to jointly encode graph structure, perform hierarchical coarsening, and generate matrix-form representations. Empirically, UGN achieves significant improvements over state-of-the-art methods on 10 out of 12 benchmark datasets. Moreover, it demonstrates strong zero-shot transferability across all six disjoint tasks, validating its universality and robust generalization capability.

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: Large pretrained models with web data

Abstract

Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. However, such generalized frameworks are not available for solving graph problems. Graph structures are ubiquitous in many applications around us and many graph problems have been widely studied over years. In recent times, there has been a surge in deep neural network based approaches to solve graph problems, with growing availability of graph structured datasets across diverse domains. Nevertheless, existing methods are mostly tailored to solve a specific task and lack the capability to create a generalized model leading to solutions for different downstream tasks. In this work, we propose a novel, resource-efficient framework named emph{U}nified emph{G}raph emph{N}etwork (UGN) by leveraging the feature extraction capability of graph convolutional neural networks (GCN) and 2-dimensional convolutional neural networks (Conv2D). UGN unifies various graph learning tasks, such as link prediction, node classification, community detection, graph-to-graph translation, knowledge graph completion, and more, within a cohesive framework, while exercising minimal task-specific extensions (e.g., formation of supernodes for coarsening massive networks to increase scalability, use of extit{mean target connectivity matrix} (MTCM) representation for achieving scalability in graph translation task, etc.) to enhance the generalization capability of graph learning and analysis. We test the novel UGN framework for six uncorrelated graph problems, using twelve different datasets. Experimental results show that UGN outperforms the state-of-the-art baselines by a significant margin on ten datasets, while producing comparable results on the remaining dataset.

Problem

• Lack of generalized frameworks for graph problems
• Proposal of UGN for unified graph learning
• Enhance generalization in graph analysis tasks

Innovation

• Unified Graph Networks (UGN)
• Leverages GCN and Conv2D
• Generalizes diverse graph tasks