Aug 2026· Social Network Analysis and Mining· 0 citations
TL;DR
In extensive experiments with over 50 real-world and randomly generated graphs, it is shown that across nearly all test cases, a member of this algorithm suite matches or surpasses h-louvain and provides a more faithful community representation than the state of the art.
Abstract
Community detection is a central problem in network science, traditionally performed on graphs, often via modularity maximization. Yet, many real-world systems inherently involve higher-order interactions between more than two entities, naturally modeled as hypergraphs. In such contexts, pairwise projections obscure higher-order structure and may lead to misleading communities. Recent works thus addressed higher-order community detection, with hypermodularity as one promising candidate. The state-of-the-art algorithm
h-louvain
[Kaminski et al., J. ComNetw 2024] mixes greedy optimization techniques for both modularity and hypermodularity in a multilevel scheme. While being effective with default parameters, the algorithm lacks guidance on how to combine the two objectives and thus needs parameter tuning. This parameter tuning step uses probabilistic strategies such as Bayesian Optimization Technique (BOT), resulting in a very significant overhead in running time. In this paper, we propose three algorithmic variants of
h-louvain
to recover communities of high accuracy without the need for time-consuming searches of mixing parameters. Our variants consist of new ideas for creating starting solutions for greedy multilevel algorithms and additional post-processing steps inspired by the community detection algorithm
leiden
[Traag et al., SciRep 2019] for graphs. In extensive experiments with over 50 real-world and randomly generated graphs, we show that across nearly all test cases, a member of our algorithm suite matches or surpasses
h-louvain
w. r. t. Asymmetric RMI; hence, they provide a more faithful community representation than the state of the art.
A novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional structural entropy within a potential game framework, and introduces a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.
Community structures are common in real networks, and extracting them provides valuable insight in applications ranging from drug discovery to market segmentation. Overlapping community detection (OCD) is the task of clustering networked data in which nodes may belong to multiple clusters. Existing OCD algorithms often struggle to achieve a suitable balance between detection quality and scalability. We, therefore, propose Highway, a scalable OCD algorithm that exploits the sparse backbone of the input network to perform efficient community inference. We used 728 Lancichinetti-Fortunato-Radicchi benchmark networks to compare Highway and its ablated version against 10 existing OCD algorithms. Our results, based on five performance measures, demonstrate a competitive performance for Highway. It ranks first in overlapping normalized mutual information with a 6.9% improvement over the strongest baseline. It also ranks second in all the other four performance measures. These comparative results suggest that Highway coupled with its backbone procedure offers a suitable accuracy-efficiency trade-off. The Highway algorithm is open-source and available as part of the CDlib library.
This work proposes two scalable greedy algorithms based on gradient-guided edge selection that efficiently approximate marginal gains and prune the candidate edge set and achieves optimization performance comparable to exact greedy strategies while substantially reducing computational time.
Runze Zhang, Gengyu Wang, Zhongzhi Zhang· Proceedings of the 32nd ACM...· 0 citations
Community detection is a fundamental task in complex network analysis, enabling the identification of hidden structures and functional groupings within social, biological, and technological systems. Although multi-objective genetic algorithms have been shown to perform well for this task, their practical application is typically constrained by excessive computational expense, especially on large or dense networks. In this paper, we present an efficient hybrid genetic algorithm for multi-objective community detection that makes a good trade-off between solution quality and computational efficiency. Our algorithm incorporates several essential improvements, including a fitness caching technique to prevent duplicate evaluations, a lightweight crossover operator to minimize the overhead of the evolutionary process, and an efficient archive management strategy that prioritizes high-quality solutions along the Pareto front. Experimental evaluations on several benchmark datasets demonstrate that the proposed method achieves comparable or improved modularity (Q) values, maintains stable normalized mutual information (NMI) values, and significantly reduces execution time compared with the previously proposed method, achieving up to 57.7% runtime reduction on large-scale networks. The results confirm that the proposed method provides a viable and scalable approach to community detection in large-scale complex networks
Mariwan Wahid Ahmed, Kamaran Hama Ali Faraj· International Journal of Art...· 0 citations
A Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner and a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs.
Mo Li, Zhaosong Zhao, Linlin Ding et al.· Annual International ACM SIG...· 0 citations
A novel directed hypergraph motif-based neural network (DHMNN) for directed hyperlink prediction, which simultaneously captures higher order structural and connectivity information from the directed hypergraph topology and significantly outperforms state-of-the-art models.
Xihang Meng, Hao Peng, Guangjie Zeng et al.· IEEE Transactions on Neural...· 0 citations