Community Detection (CD) in weighted social networks is a highly active research field, celebrated for its profound practical implications across a multitude of disciplines. Genetic algorithms (GAs) are frequently explored to tackle CD problems, leveraging their capability to navigate the extensive discrete search space effectively. Throughout the evolutionary process, genetic operators such as crossover and mutation assume pivotal roles in effectively exploring the vast solution space. Nonetheless, prevailing GA-based approaches often ignore crucial topology information, particularly information regarding edge weights, resulting in compromised algorithm performance. In light of this, this paper introduces Edge Information-based GA (EIGA) to effectively solve CD problems in weighted networks. This is achieved specifically through the innovative designs of edgeweight-aware crossover and mutation operators. These novel edge-weight-aware operators improve the extraction of meaningful community structures, advancing knowledge discovery from social networks. Empirical findings demonstrate the superior performance of EIGA over numerous state-of-the-art algorithms across various real-world and synthetic benchmark networks.

Community detection, a vital technology for real-world applications, uncovers cohesive node groups (communities) by leveraging both topological and attribute similarities in social networks. However, existing Graph Convolutional Networks (GCNs) trained to maximize modularity often converge to suboptimal solutions. Additionally, directly using human-labeled communities for training can undermine topological cohesiveness by grouping disconnected nodes based solely on node attributes. We address these issues by proposing a novel Topological and Attributive Similarity-based Community detection (TAS-Com) method. TAS-Com introduces a novel loss function that exploits the highly effective and scalable Leiden algorithm to detect community structures with global optimal modularity. Leiden is further utilized to refine human-labeled communities to ensure connectivity within each community, enabling TAS-Com to detect community structures with desirable trade-offs between modularity and compliance with human labels. Experimental results on multiple benchmark networks confirm that TAS-Com can significantly outperform several state-of-the-art algorithms.