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New Benchmark Evaluates Graph Reduction Impact on Influence Maximization

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the Spreading-Oriented Reduction Benchmark (SORB), an open-source framework designed to evaluate influence maximization (IM) models. SORB integrates graph reduction techniques directly into the evaluation process, allowing for a more comprehensive assessment of IM algorithms across various real-world network types and task settings. Initial studies using SORB indicate that the effectiveness of graph reduction is highly dependent on the network structure and the specific downstream task, with sparsification proving beneficial for single-layer networks but leading to degradation on flattened multilayer networks. AI

RANK_REASON The cluster contains an academic paper introducing a new benchmark and evaluation framework for graph reduction in multirelational networks. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Benchmark Evaluates Graph Reduction Impact on Influence Maximization

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The cluster contains an academic paper introducing a new benchmark and evaluation framework for graph reduction in multirelational networks. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mateusz Stolarski, Micha{\l} Czuba, Piotr Bielak, Piotr Br\'odka ·

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

    arXiv:2606.12581v1 Announce Type: cross 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 influenc…

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