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
The modular organization of brain cortical connectivity across the human lifespan.
Maria Grazia Puxeddu,Joshua Faskowitz,Richard F. Betzel,Manuela Petti,Laura Astolfi,Olaf Sporns +5 more
TL;DR: This work examined the brain's modular organization by developing an ensemble-based multilayer network approach, allowing us to link changes of structural connectivity patterns to development and aging and shows that modular structure exhibits both linear and nonlinear age-related trends.
Journal ArticleDOI
Multiscale dynamical embeddings of complex networks.
Michael T. Schaub,Michael T. Schaub,Jean-Charles Delvenne,Renaud Lambiotte,Mauricio Barahona +4 more
TL;DR: A time-dependent dynamical similarity measure between nodes is proposed, which quantifies the effect a node-input has on the network and induces an embedding that can be employed for several analysis tasks.
Journal ArticleDOI
Multilink communities of multiplex networks.
TL;DR: This work uncovers the rich community structure of multiplex networks by associating a community to each multilink where the multilinks characterize the connections existing between any two nodes of the multiplex network.
Journal ArticleDOI
Community structure: A comparative evaluation of community detection methods
TL;DR: This paper provides comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimization schemes as well as a comparison of their partitioning strategy through validation metrics, and proposes ways to classify community detection methods.
Journal ArticleDOI
Post-processing partitions to identify domains of modularity optimization
TL;DR: The Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm as mentioned in this paper identifies the domain of modularity optimization for each partition and discard partitions with empty domains to obtain the subset of partitions that are admissible candidate community structures that remain potentially optimal over indicated parameter domains.
References
More filters
Journal ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
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.
Journal ArticleDOI
Finding and evaluating community structure in networks.
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Journal ArticleDOI
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.