Introduces the SORB benchmark showing that sparsification and coarsening effects on influence maximization performance depend strongly on network type and evaluation metric.
Identifying super spreaders in multilayer networks,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ts-net trained solely on synthetic multilayer networks achieves zero-shot generalization to real-world multilayer networks for super-spreader identification, outperforming heuristics and transductive baselines on three of four metrics.
citing papers explorer
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Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark
Introduces the SORB benchmark showing that sparsification and coarsening effects on influence maximization performance depend strongly on network type and evaluation metric.
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Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks
ts-net trained solely on synthetic multilayer networks achieves zero-shot generalization to real-world multilayer networks for super-spreader identification, outperforming heuristics and transductive baselines on three of four metrics.