CriticalSet identifies the k contributors whose removal isolates the largest number of items in a bipartite dependency network, solved via ShapleyCov centrality derived from the Shapley value and the linear-time MinCov peeling algorithm.
In: Proceedings of the 22nd international conference on world wide web
2 Pith papers cite this work. Polarity classification is still indexing.
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SP-GCRL combines a nonlinear social diffusion model, dual-view contrastive learning for robust node embeddings, a GAT surrogate, and DDQN to learn end-to-end seed selection policies for influence maximization under partial graph observability.
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The CriticalSet problem: Identifying Critical Contributors in Bipartite Dependency Networks
CriticalSet identifies the k contributors whose removal isolates the largest number of items in a bipartite dependency network, solved via ShapleyCov centrality derived from the Shapley value and the linear-time MinCov peeling algorithm.
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SP-GCRL: Influence Maximization on Incomplete Social Graphs
SP-GCRL combines a nonlinear social diffusion model, dual-view contrastive learning for robust node embeddings, a GAT surrogate, and DDQN to learn end-to-end seed selection policies for influence maximization under partial graph observability.