Community detection in networks: A multidisciplinary review

TL;DR: This review systematically surveys community detection methods in hybrid networks with multiple nodes and relationships, analyzing existing methods' limitations, challenges, and future trends, and evaluates common metrics such as NMI, ARI, and Q for three application scenarios.

TL;DR: Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach and can achieve suitable community partition effect on online social network data sets.

TL;DR: This work analyzes flow networks tuned to perform complex multifunctional tasks and finds that the response of such networks encodes hidden topological features that provide a universal topological description for all networks that perform these types of functions.

TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.

TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.

TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.