Multi-Label Node Classification with Label Influence Propagation

• URL: http://arxiv.org/abs/2607.00671v1
• Date: Wed, 01 Jul 2026 09:17:28 GMT
• Title: Multi-Label Node Classification with Label Influence Propagation
• Abstract summary: Tackling multi-label node classification (MLNC) on graphs has led to the development of various approaches.<n>Some methods leverage graph neural networks (GNNs) to exploit label co-occurrence correlations, while others incorporate label embeddings to capture label proximity.<n>We decompose the message passing process in GNNs into two operations: propagation and transformation.<n>We then conduct a comprehensive analysis and quantification of the influence correlations between labels in each operation.
• Score: 79.38415948442751
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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