Tracking the Evolution of Communities in Dynamic Social Networks
By Derek Greene, Dónal Doyle, Padraig Cunningham. International Conference on Advances in Social Networks Analysis and Mining, 2010
547 citations recorded by the source corpus.
Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.
Subfields: Statistical and Nonlinear Physics, Transportation
View Paper: https://www.semanticscholar.org/paper/7341b65cad3300a86068e01a9848555e43ff8f19
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistical and Nonlinear Physics
- David Camacho
- Daniel Molina
- Carlos Artemio Coello Coello
- Ponnuthurai Nagaratnam Suganthan
- Francisco Herrera
- Punam Bedi
- Swagatam Das
- Chhavi Sharma
In Transportation
Citations in the corpus
- Bio-inspired computation: Where we stand and what's next (2019, background): “nodes [357], or studying the dynamics of a network when modeling a non-stationary information source [358].”
- Community detection in social networks (2016, methodology): “Greene et al. 104 proposed a heuristic technique for the identi fi cation of dynamic communities in the network data.”
- Community Discovery in Dynamic Networks (2017, unknown): “Methods in this subcategory: (Bóta et al., 2011; Bourqui et al., 2009; Bródka et al., 2013; Dhouioui and Akaichi, 2014; Greene et al., 2010; Hopcroft et al., 2004; İlhan and Öğüdücü, 2015; Palla et al., 2007; Rosvall and Bergstrom, 2010; Takaffoli et al., 2011).”