Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

The Spreading-Oriented Reduction Benchmark (SORB) is an open-source framework designed to systematically evaluate influence maximization (IM) models in complex networks, specifically quantifying how graph reduction preprocessing impacts predictive performance. Addressing the computational challenges of large, incomplete, and noisy real-world networks, SORB integrates graph reduction directly into its evaluation pipeline, utilizing a collection of 7 single- and multilayer networks. Experiments with 5 sparsification and 2 coarsening methods across 6 IM models reveal that reduction's impact varies significantly with network type and downstream task, such as Gain@k or AUC_cutoff. Sparsification generally preserves seed set quality on single-layer networks, while flattened multilayer networks exhibit systematic ranking degradation. Crucially, performance degradation is primarily driven by the chosen reduction strategy, not the nominal reduction rate, and sparsification measurably reduces inference time and memory for computationally intensive models like ts-net and v-rnk-m.

Key takeaway

For Machine Learning Engineers deploying influence maximization models on large, complex networks, you should strategically integrate graph sparsification as a preprocessing step. This can significantly reduce memory and inference time for computationally intensive models like ts-net and v-rnk-m, particularly on homogeneous graphs. However, when working with multilayer networks, be cautious of performance degradation introduced by flattening transformations; prioritize reduction strategies over nominal rates. You should also explore dedicated multilayer reduction algorithms to fully realize efficiency gains without compromising accuracy.

Key insights

Graph reduction's impact on influence maximization performance varies significantly by network type and reduction strategy.

Principles

• Reduction impact is network- and task-dependent.
• Reduction strategy outweighs nominal rate for performance.
• Flattening multilayer networks can degrade ranking.

Method

SORB preprocesses networks via reduction, runs IM models, simulates diffusion on original graphs, then evaluates outcomes with Gain@k and AUC_cutoff.

In practice

• Sparsification can reduce IM model memory/time.
• Use reduction-aware evaluation for spreading processes.
• Consider v-rnk-m with LocalDegree for robustness.

Topics

• Graph Reduction
• Influence Maximization
• Multilayer Networks
• Network Benchmarking
• Sparsification
• Computational Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.