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Found 7 papers, 1 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.
Randomized Algorithms for Symmetric Nonnegative Matrix Factorization
The second algorithm uses randomized leverage score sampling to approximately solve constrained least squares problems.
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.
Seven open problems in applied combinatorics
We present and discuss seven different open problems in applied combinatorics.
Skew-Symmetric Adjacency Matrices for Clustering Directed Graphs
Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections, similar to cut-based undirected graph clustering methods.
Hypergraph Random Walks, Laplacians, and Clustering
We propose a flexible framework for clustering hypergraph-structured data based on recently proposed random walks utilizing edge-dependent vertex weights.
Relative Hausdorff Distance for Network Analysis
Similarity measures are used extensively in machine learning and data science algorithms.
