Novel Algorithms for Efficient Mining of Connected Induced Subgraphs of a Given Cardinality
Shanshan Wang, Chenglong Xiao

TL;DR
This paper introduces two novel algorithms for efficiently enumerating all connected induced subgraphs of a specified size in networks, improving performance especially for large subgraph sizes through top-down and bottom-up approaches.
Contribution
The paper presents a new top-down enumeration algorithm and an improved bottom-up variant, with theoretical analysis and experimental validation showing superior efficiency over existing methods.
Findings
Bottom-up algorithm outperforms existing algorithms for small subgraph sizes.
Top-down algorithm achieves up to tenfold speedup for large subgraph sizes.
Algorithms are validated on real-world network datasets.
Abstract
Mining subgraphs with interesting structural properties from networks (or graphs) is a computationally challenging task. In this paper, we propose two algorithms for enumerating all connected induced subgraphs of a given cardinality from networks (or connected undirected graphs in networks). The first algorithm is a variant of a previous well-known algorithm. The algorithm enumerates all connected induced subgraphs of cardinality in a bottom-up manner. The data structures that lead to unit time element checking and linear space are presented. Different from previous algorithms that either work in a bottom-up manner or a reverse search manner, an algorithm that enumerates all connected induced subgraphs of cardinality in a top-down manner is proposed. The correctness and complexity of the top-down algorithm are theoretically analyzed and proven. In the experiments, we evaluate…
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Taxonomy
TopicsData Mining Algorithms and Applications · Complex Network Analysis Techniques · Rough Sets and Fuzzy Logic
