On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithms
June 10, 2023 Β· Declared Dead Β· π Knowledge Discovery and Data Mining
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Authors
Fanchen Bu, Kijung Shin
arXiv ID
2306.06368
Category
cs.SI: Social & Info Networks
Cross-listed
cs.DS
Citations
5
Venue
Knowledge Discovery and Data Mining
Last Checked
4 months ago
Abstract
Graphs are a powerful mathematical model, and they are used to represent real-world structures in various fields. In many applications, real-world structures with high connectivity and robustness are preferable. For enhancing the connectivity and robustness of graphs, two operations, adding edges and anchoring nodes, have been extensively studied. However, merging nodes, which is a realistic operation in many scenarios (e.g., bus station reorganization, multiple team formation), has been overlooked. In this work, we study the problem of improving graph cohesiveness by merging nodes. First, we formulate the problem mathematically using the size of the $k$-truss, for a given $k$, as the objective. Then, we prove the NP-hardness and non-modularity of the problem. After that, we develop BATMAN, a fast and effective algorithm for choosing sets of nodes to be merged, based on our theoretical findings and empirical observations. Lastly, we demonstrate the superiority of BATMAN over several baselines, in terms of speed and effectiveness, through extensive experiments on fourteen real-world graphs.
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