Towards Direct Comparison of Community Structures in Social Networks
Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. However, while comparing the metric values it loses direct involvement of topological information of the communities in the comparison process. In this paper, a direct comparison approach is proposed where topological information of the communities obtained with two algorithms are compared directly. A quality measure namely \emph{Topological Variance (TV)} is designed based on direct comparison of topological information of the communities. Considering the newly designed quality measure, two ranking schemes are developed. The efficacy of proposed quality metric as well as the ranking scheme is studied with eight widely used real-world datasets and six community detection algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Community DetectionSimilar Papers 제목 키워드 기반
Direct Comparative Analysis of Nature-inspired Optimization Algorithms on Community Detection Problem in Social Networks
Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the object…
Community DetectionCommunity Norms in the Spotlight: Enabling Task-Agnostic Unsupervised Pre-Training to Benefit Online Social Media
Modelling the complex dynamics of online social platforms is critical for addressing challenges such as hate speech and misinformation. While Discussion Transformers, which model conversations as graph structures, have e…
Unsupervised Pre-trainingCC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks
A community structure is an integral part of a social network. Detecting such communities plays an important role in a wide range of applications, including but not limited to cluster analysis, recommendation systems a…
Recommendation SystemsDetecting Local Community Structures in Social Networks Using Concept Interestingness
One key challenge in Social Network Analysis is to design an efficient and accurate community detection procedure as a means to discover intrinsic structures and extract relevant information. In this paper, we introduce …
Community DetectionGraph Integrated Transformers for Community Detection in Social Networks
Community detection is crucial for applications like targeted marketing and recommendation systems. Traditional methods rely on network structure, and embedding-based models integrate semantic information. However, there…
Recommendation SystemsCommunity Detection