6 papers
A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers
Ryan DeWolfe, PaweÅ PraÅat, François Théberge
Clustering algorithms are an essential part of the unsupervised data science ecosystem, and extrinsic evaluation of clustering algorithms requires a method for comparing the detect…
Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding
Ryan DeWolfe
Leveraging non-linear dimension reduction techniques, we remove the low dimension constraint from node embedding and propose COVE, an explainable high dimensional embedding that, w…
The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+)
Jordan Barrett, Ryan DeWolfe, BogumiÅ KamiÅski +3
The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The…
Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Ryan DeWolfe
Most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a pa…
Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Ryan DeWolfe, François Théberge
Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge…
Improving community detection via community association strength scores
Jordan Barrett, Ryan DeWolfe, BogumiÅ KamiÅski +3
Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms ge…