Clustering Edges in Directed Graphs

• URL: http://arxiv.org/abs/2202.12265v1
• Date: Wed, 23 Feb 2022 16:24:55 GMT
• Title: Clustering Edges in Directed Graphs
• Authors: Manohar Murthi and Kamal Premaratne
• Abstract summary: We develop a framework for edge clustering, a new method for exploratory data analysis. Edges sharing a functional affinity are assigned to the same group and form an influence subgraph cluster. We present several diverse examples demonstrating the potential for widespread application of edge clustering in scientific research.
• Score: 0.40611352512781856
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: How do vertices exert influence in graph data? We develop a framework for edge clustering, a new method for exploratory data analysis that reveals how both vertices and edges collaboratively accomplish directed influence in graphs, especially for directed graphs. In contrast to the ubiquitous vertex clustering which groups vertices, edge clustering groups edges. Edges sharing a functional affinity are assigned to the same group and form an influence subgraph cluster. With a complexity comparable to that of vertex clustering, this framework presents three different methods for edge spectral clustering that reveal important influence subgraphs in graph data, with each method providing different insight into directed influence processes. We present several diverse examples demonstrating the potential for widespread application of edge clustering in scientific research.

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