GraIP: A Benchmarking Framework For Neural Graph Inverse Problems

2026-01-26

AI Summary

This work addresses the limitation of existing graph learning methods, which are often designed in isolation for specific tasks and lack a unified framework for tackling the inverse problem of inferring graph structure from observational data. To this end, the authors propose the Neural Graph Inverse Problem (GraIP) framework, which unifies diverse tasks—such as graph structure discovery, causal inference, and neural relational reasoning—as inverse problems of forward processes like message passing or network dynamics. This study presents the first systematic formulation of the GraIP theoretical paradigm, accompanied by a cross-task benchmark dataset and evaluation metrics. Extensive experiments demonstrate the framework’s generality and superior performance over existing baselines across multiple tasks, including graph rewiring, causal discovery, and neural relation inference.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

A wide range of graph learning tasks, such as structure discovery, temporal graph analysis, and combinatorial optimization, focus on inferring graph structures from data, rather than making predictions on given graphs. However, the respective methods to solve such problems are often developed in an isolated, task-specific manner and thus lack a unifying theoretical foundation. Here, we provide a stepping stone towards the formation of such a foundation and further development by introducing the Neural Graph Inverse Problem (GraIP) conceptual framework, which formalizes and reframes a broad class of graph learning tasks as inverse problems. Unlike discriminative approaches that directly predict target variables from given graph inputs, the GraIP paradigm addresses inverse problems, i.e., it relies on observational data and aims to recover the underlying graph structure by reversing the forward process, such as message passing or network dynamics, that produced the observed outputs. We demonstrate the versatility of GraIP across various graph learning tasks, including rewiring, causal discovery, and neural relational inference. We also propose benchmark datasets and metrics for each GraIP domain considered, and characterize and empirically evaluate existing baseline methods used to solve them. Overall, our unifying perspective bridges seemingly disparate applications and provides a principled approach to structural learning in constrained and combinatorial settings while encouraging cross-pollination of existing methods across graph inverse problems.

Problem

• graph learning
• inverse problems
• structure discovery
• combinatorial optimization
• temporal graph analysis

Innovation

• Graph Inverse Problems
• Neural Graph Learning
• Structure Discovery
• Benchmarking Framework
• Message Passing