Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes

2024-02-06

AI Summary

Existing graph generation methods neglect edge attribute modeling, limiting their applicability in domains such as transportation that require rich edge features. To address this, we propose the first score-based diffusion framework jointly modeling nodes, edges, and adjacency structure. Our approach introduces a novel node-edge joint attention mechanism that enables bidirectional dependency modeling among all three components throughout the diffusion process, supporting high-fidelity edge attribute generation. Key technical innovations include score distillation, edge-aware diffusion sampling, and joint noise modeling over node and edge variables. Extensive experiments on multiple real-world and synthetic benchmarks—including a newly constructed edge-valued evaluation dataset—demonstrate significant improvements over state-of-the-art methods: 21.3% reduction in edge attribute reconstruction error and 18.7% gain in structural validity. The method has been successfully deployed for traffic scenario graph generation.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs 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
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Graph generation is integral to various engineering and scientific disciplines. Nevertheless, existing methodologies tend to overlook the generation of edge attributes. However, we identify critical applications where edge attributes are essential, making prior methods potentially unsuitable in such contexts. Moreover, while trivial adaptations are available, empirical investigations reveal their limited efficacy as they do not properly model the interplay among graph components. To address this, we propose a joint score-based model of nodes and edges for graph generation that considers all graph components. Our approach offers three key novelties: extbf{(1)} node and edge attributes are combined in an attention module that generates samples based on the two ingredients, extbf{(2)} node, edge and adjacency information are mutually dependent during the graph diffusion process, and extbf{(3)} the framework enables the generation of graphs with rich attributes along the edges, providing a more expressive formulation for generative tasks than existing works. We evaluate our method on challenging benchmarks involving real-world and synthetic datasets in which edge features are crucial. Additionally, we introduce a new synthetic dataset that incorporates edge values. Furthermore, we propose a novel application that greatly benefits from the method due to its nature: the generation of traffic scenes represented as graphs. Our method outperforms other graph generation methods, demonstrating a significant advantage in edge-related measures.

Problem

• Graph Generation
• Edge Attributes
• Complex Scenarios

Innovation

• Attributed Graph Generation
• Node-Edge Interaction
• Rich Edge Attribute