Preserving Core Structures of Social Networks via Information Guided Multi-Step Graph Pruning

2025-10-12

AI Summary

Dense, overlapping connections in social networks obscure the underlying structural backbone, impeding community detection, information flow analysis, and functional interpretation. To address this, we propose IGPrune—a novel information-guided, differentiable multi-step graph pruning framework. IGPrune quantifies edge relevance to downstream tasks via mutual information and incorporates gradient-boosting principles into an end-to-end differentiable optimization mechanism, enabling dynamic, progressive removal of redundant edges. Crucially, it jointly optimizes structural simplification and semantic preservation within graph neural networks. Extensive experiments on social and biological networks demonstrate that IGPrune maintains near-identical task performance post-pruning while substantially enhancing model interpretability. It successfully extracts a compact, interpretable backbone structure that faithfully reflects community organization and functional associations—thereby bridging structural sparsification with semantic fidelity in graph representation learning.

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: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Social networks often contain dense and overlapping connections that obscure their essential interaction patterns, making analysis and interpretation challenging. Identifying the structural backbone of such networks is crucial for understanding community organization, information flow, and functional relationships. This study introduces a multi-step network pruning framework that leverages principles from information theory to balance structural complexity and task-relevant information. The framework iteratively evaluates and removes edges from the graph based on their contribution to task-relevant mutual information, producing a trajectory of network simplification that preserves most of the inherent semantics. Motivated by gradient boosting, we propose IGPrune, which enables efficient, differentiable optimization to progressively uncover semantically meaningful connections. Extensive experiments on social and biological networks show that IGPrune retains critical structural and functional patterns. Beyond quantitative performance, the pruned networks reveal interpretable backbones, highlighting the method's potential to support scientific discovery and actionable insights in real-world networks.

Problem

• Identifying essential structural backbone in dense social networks
• Balancing network complexity with task-relevant information preservation
• Uncovering interpretable core connections through differentiable graph pruning

Innovation

• Multi-step pruning framework balances complexity and information
• IGPrune enables differentiable optimization for semantic connections
• Information theory guides edge removal while preserving semantics