Community Discovery in Dynamic Networks
By Giulio Rossetti, Rémy Cazabet. ACM Computing Surveys, 2017
295 citations recorded by the source corpus.
Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.
Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/ce0c2a4d5c72d28c8ceb94362e3350eff0889a10
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Networks and Communications
In Statistical and Nonlinear Physics
Citations in the corpus
- A survey of community search over big graphs (2019, methodology): “on in dynamic networks. In [117], Kuncheva et al. presented a method by using spectral graph wavelets to detect communities in temporal graphs. For more related studies, please refer to survey papers [164,178]. 10.2 Cohesive SubGraph Discovery In this section, we review studies on cohesive subgraph ”
- Nine quick tips for analyzing network data (2019, background): “, rewiring of interactions or changes in network metrics over time), and extensions of the concepts developed in Tip 6 now exist in the dynamic case [43, 44].”