Multi-Label Node Classification with Label Influence Propagation

2026-07-01

AI Summary

This work addresses the challenge of effectively modeling complex inter-label dependencies in multi-label node classification on graph-structured data. The authors propose a novel approach that decouples the message-passing mechanism of graph neural networks into distinct propagation and transformation operations, enabling explicit analysis and quantification of positive and negative interactions among labels. Building upon this decomposition, they construct a label influence graph to propagate higher-order label effects and introduce a dynamic adjustment mechanism to optimize the learning process. To the best of our knowledge, this is the first method to systematically model inter-label influence relations in non-Euclidean graph data. Extensive experiments demonstrate that the proposed framework significantly outperforms state-of-the-art baselines across multiple benchmark datasets, achieving substantial improvements in multi-label node classification performance.

Abstract

Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI networks with multiple functions and users in social or e-commerce networks exhibiting diverse interests. Tackling multi-label node classification (MLNC) on graphs has led to the development of various approaches. Some methods leverage graph neural networks (GNNs) to exploit label co-occurrence correlations, while others incorporate label embeddings to capture label proximity. However, these approaches fail to account for the intricate influences between labels in non-Euclidean graph data. To address this issue, we decompose the message passing process in GNNs into two operations: propagation and transformation. We then conduct a comprehensive analysis and quantification of the influence correlations between labels in each operation. Building on these insights, we propose a novel model, Label Influence Propagation (LIP). Specifically, we construct a label influence graph based on the integrated label correlations. Then, we propagate high-order influences through this graph, dynamically adjusting the learning process by amplifying labels with positive contributions and mitigating those with negative influence. Finally, our framework is evaluated on comprehensive benchmark datasets, consistently outperforming SOTA methods across various settings, demonstrating its effectiveness on MLNC tasks.

Problem

• multi-label node classification
• label influence
• graph neural networks
• non-Euclidean graph data
• label correlation

Innovation

• Label Influence Propagation
• Multi-Label Node Classification
• Graph Neural Networks
• Label Correlation
• Message Passing