The many facets of community detection in complex networks.
Michael T. Schaub,Michael T. Schaub,Michael T. Schaub,Jean-Charles Delvenne,Martin Rosvall,Renaud Lambiotte +5 more
Reads0
Chats0
TLDR
In this paper, the authors provide a focused review of the different motivations that underpin community detection, highlighting the different facets of community detection and highlighting the many lines of research and points out open directions and avenues for future research.Abstract:
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.read more
Citations
More filters
Journal ArticleDOI
Multiresolution Consensus Clustering in Networks
TL;DR: In this paper, the authors propose a method for identifying community structure at different scales based on multiresolution modularity and consensus clustering, which can be applied to the output of any clustering algorithm.
Journal ArticleDOI
Simplicial complexes and complex systems
TL;DR: This work presents strengths and weaknesses of current methods, as well as a range of empirical studies relevant to the field of complex systems, before identifying future methodological challenges to help understand the emergence of collective phenomena.
Journal ArticleDOI
Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian
TL;DR: Using graphs to model pairwise relationships between entities is a ubiquitous framework for studying complex systems and data and Simplicial complexes extend this dyadic model of graphs to polyadic structures.
Journal ArticleDOI
Generative models for network neuroscience: prospects and promise.
TL;DR: Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand how neural networks are connected.
Journal ArticleDOI
A review of stochastic block models and extensions for graph clustering
TL;DR: Different approaches and extensions proposed for different aspects in model-based clustering of graphs, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated are reviewed.
References
More filters
Journal ArticleDOI
Generative models for network neuroscience: prospects and promise.
TL;DR: Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand how neural networks are connected.
Journal ArticleDOI
Phase transition in the economically modeled growth of a cellular nervous system
Vincenzo Nicosia,Petra E. Vértes,William R Schafer,Vito Latora,Vito Latora,Edward T. Bullmore,Edward T. Bullmore +6 more
TL;DR: This work uses graph analysis and generative modeling to show that the transition between different growth regimes, as well as its coincidence with the moment of hatching, may be explained by a dynamic economical model that incorporates a tradeoff between topology and cost that is continuously negotiated and renegotiated over developmental time.
Posted Content
Generative Models for Network Neuroscience: Prospects and Promise
TL;DR: This work reviews generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human.
Posted Content
A New Metric for Quality of Network Community Structure
TL;DR: This work proposes to modify modularity by subtracting from it the fraction of edges connecting nodes of different communities and by including community density into modularity, and describes the motivation for introducing this metric and proves that this new metric solves the resolution limit problem.