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Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning
Meng Liu, Nate Veldt, Haoyu Song +2
Hypergraph-based machine learning methods are now widely recognized as important for modeling and using higher-order and multiway relationships between data objects. Local hypergra…
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
Meng Liu, David F. Gleich
Graph based semi-supervised learning is the problem of learning a labeling function for the graph nodes given a few example nodes, often called seeds, usually under the assumption…
Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance
K. Fountoulakis, M. Liu, D. F. Gleich +1
Clustering points in a vector space or nodes in a graph is a ubiquitous primitive in statistical data analysis, and it is commonly used for exploratory data analysis. In practice,…
Local Hypergraph Clustering using Capacity Releasing Diffusion
Rania Ibrahim, David F. Gleich
Local graph clustering is an important machine learning task that aims to find a well-connected cluster near a set of seed nodes. Recent results have revealed that incorporating hi…
Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs
Nate Veldt, Anthony Wirth, David F. Gleich
Motivated by applications in community detection and dense subgraph discovery, we consider new clustering objectives in hypergraphs and bipartite graphs. These objectives are param…