Community detection in networks: A multidisciplinary review

TL;DR: A comprehensive review of the latest progress in community detection through deep learning is presented in this paper , where the authors have devised a new taxonomy covering different state-of-theart methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering.

TL;DR: This study proposes a Pointwise mutual information-incorporated and Graph-regularized SNMF (PGS) model, which uses Pointwise Mutual Information to quantify implicit associations among nodes, thereby completing the missing but crucial information among critical nodes in a uniform way.

TL;DR: A comprehensive taxonomy of machine learning techniques for in-network caching in edge networks is formulated and a comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy.

TL;DR: A community detection algorithm based on graph compression for the full topology of an original social network, which demonstrates the superiority of this proposal compared to several existing state-of-the-art community detection algorithms.

TL;DR: A large class of models, including several generalizations of stochastic block models, as well as models parameterizing global tendencies towards clustering and centralization, and individual differences in such tendencies are described and extended.

TL;DR: The empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality, and would not recommend PCA before clustering except in special circumstances.

TL;DR: Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs, to use to gain a better understanding of brain disease.

TL;DR: SimBet Routing is proposed which exploits the exchange of pre-estimated "betweenness' centrality metrics and locally determined social "similarity' to the destination node and outperforms PRoPHET Routing, particularly when the sending and receiving nodes have low connectivity.