@article{Wang2025, 
author = {Shan-Shan Wang and Cheng-Long Xiao},
title = {Novel Algorithms for Efficient Mining of Connected Induced Subgraphs of a Given Cardinality},
year = {2025},
journal = {Journal of Computer Science and Technology},
volume = {40},
number = {2},
pages = {428-443},
keywords = {graph theory, subgraph enumeration, connected induced subgraph, top-down search},
url = {https://www.sciopen.com/article/10.1007/s11390-024-3039-2},
doi = {10.1007/s11390-024-3039-2},
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 k 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 work in either a bottom-up manner or a reverse search manner, an algorithm that enumerates all connected induced subgraphs of cardinality k 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 the efficiency of the algorithms using a set of real-world networks from various fields. Experimental results show that the variant bottom-up algorithm outperforms the state-of-the-art algorithms for enumerating connected induced subgraphs of small cardinality, and the top-down algorithm can achieve an order of magnitude speedup over the state-of-the-art algorithms for enumerating connected induced subgraphs of large cardinality.}
}