New Benchmark Framework Evaluates Graph Reduction Effects on Influence Maximization in Complex Networks
Researchers have introduced SORB (Spreading-Oriented Reduction Benchmark), an open-source framework for evaluating how graph reduction techniques affect influence maximization (IM) models in real-world networks. The study examines sparsification and coarsening as preprocessing steps across single-layer and multilayer network structures. The findings reveal that reduction strategies can systematically degrade predictive performance in multilayer networks, underscoring the need for reduction-aware evaluation pipelines.
A new preprint posted to arXiv introduces SORB, a standardized, extensible benchmark designed to evaluate influence maximization algorithms while explicitly accounting for graph reduction as a preprocessing step. Influence maximization—the problem of identifying which nodes in a network best spread information or influence—is computationally demanding at scale, and graph reduction is commonly used to make networks more tractable. Prior work has largely studied IM algorithms in isolation, without systematically measuring how reduction alters their outputs. Using SORB, the authors tested sparsification and coarsening strategies across diverse real-world networks and found that their impact depends heavily on both network type and evaluation metric. Sparsification largely preserves seed set quality in single-layer networks, but flattened multilayer networks show consistent ranking degradation regardless of which reduction method is applied. The authors argue these results demonstrate that multi-task, reduction-aware evaluation is essential when studying spreading processes in complex, multirelational networks. The framework is open-source and intended to serve as a community standard for future IM research.
What's missing
The study is a preprint and has not yet undergone peer review. The paper does not report results on dynamic or temporally evolving networks despite noting that real-world networks are dynamically evolving.
What different sources said
- arXiv cs.AICenter
Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark
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