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

2026-06-10

AI Summary

The role of graph reduction in influence maximization (IM), particularly within multirelational networks, remains underexplored. This work proposes SORB, the first propagation-oriented benchmark for evaluating graph reduction strategies in conjunction with IM within a unified assessment framework, and establishes a scalable experimental pipeline encompassing both single-layer and multilayer real-world networks. By integrating graph sparsification and coarsening techniques, the study systematically investigates how various reduction methods affect downstream IM performance. The findings reveal that sparsification effectively preserves seed set quality in single-layer networks, whereas flattening multilayer networks—regardless of the reduction strategy employed—consistently leads to substantial degradation in ranking performance. These results underscore that the efficacy of graph reduction is highly contingent on both network structure and task objectives, highlighting the necessity of adopting reduction-aware, multitask evaluation paradigms.

Abstract

Real-world networks are inherently incomplete, noisy, and dynamically evolving, making it difficult to capture all actors and their relationships. Their scale often renders direct analysis computationally demanding. While influence maximisation (IM) has been widely studied, the role of graph reduction as a preprocessing step, and its impact on IM accuracy, remains underexplored. In this work, we introduce the Spreading-Oriented Reduction Benchmark (SORB), an open-source, standardised framework for systematically evaluating IM models across diverse task settings. SORB provides an extensible pipeline operating on a representative collection of real-world networks, including single- and multilayer structures, and accounts for graph reduction directly into the evaluation process. This design shifts the focus from analysing IM algorithms in isolation to quantifying how graph reduction alters predictive performance. Using SORB, we study the effects of sparsification and coarsening across multiple IM scenarios. Our results show that the impact of reduction is strongly dependent on both the network type (single-layer vs. multirelational) and the downstream task ($Gain@k$ vs. $\mathrm{AUC}_{\mathrm{cutoff}}$): sparsification preserves seed set quality on single-layer networks, whereas flattened multilayer networks exhibit systematic ranking degradation regardless of reduction strategy. These findings highlight the importance of reduction-aware, multi-task evaluation when studying spreading processes in complex networks.

Problem

• graph reduction
• influence maximisation
• multirelational networks
• spreading processes
• evaluation benchmark

Innovation

• graph reduction
• influence maximisation
• multirelational networks
• spreading-oriented benchmark
• SORB