New Benchmark Evaluates Graph Reduction Impact on Influence Maximization

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 →

• graph reduction
• Hugging Face
• influence maximisation
• multilayer networks
• multirelational networks
• single-layer networks
• Spreading-Oriented Reduction Benchmark

• paper
• other

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