A Comprehensive Review of Community Detection in Graphs
The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs.
Code (0)
등록된 구현이 없습니다.
Tasks
Community DetectionSociologySimilar Papers 제목 키워드 기반
Out-Of-Distribution Generalization on Graphs: A Survey
Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis…
Out-of-Distribution GeneralizationSurveyClustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is…
ClusteringCommunity DetectionGraph ClusteringSociology+1A Survey of Visual Sensory Anomaly Detection
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semantic anomaly detection which detects anomal…
Anomaly DetectionSurveyAutomated Machine Learning on Graphs: A Survey
Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly dif…
AutoMLBIG-bench Machine LearningGraph LearningNeural Architecture Search+1Word Embedding-based Text Processing for Comprehensive Summarization and Distinct Information Extraction
In this paper, we propose two automated text processing frameworks specifically designed to analyze online reviews. The objective of the first framework is to summarize the reviews dataset by extracting essential sentenc…
ClusteringCommunity DetectionQuestion AnsweringSentence+1