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Found 6 papers, 4 papers with code
HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network
In data science, hypergraphs are natural models for data exhibiting multi-way relations, whereas graphs only capture pairwise.
A Tri-Level Optimization Model for Interdependent Infrastructure Network Resilience Against Compound Hazard Events
Here, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations.
Scalable tensor methods for nonuniform hypergraphs
While multilinear algebra appears natural for studying the multiway interactions modeled by hypergraphs, tensor methods for general hypergraphs have been stymied by theoretical and practical barriers.
Hypergraph Clustering for Finding Diverse and Experienced Groups
In contrast to related problems on fair or balanced clustering, we model diversity in terms of variety of past experience (instead of, e. g., protected attributes), with a goal of forming groups that have both experience and diversity with respect to participation in edge types.
Clustering in graphs and hypergraphs with categorical edge labels
Here, we develop a computational framework for the problem of clustering hypergraphs with categorical edge labels --- or different interaction types --- where clusters corresponds to groups of nodes that frequently participate in the same type of interaction.
Planted Hitting Set Recovery in Hypergraphs
In various application areas, networked data is collected by measuring interactions involving some specific set of core nodes.
