A framework for community identification in dynamic social networks

By Chayant Tantipathananandh, Tanya Y. Berger-Wolf, David Kempe. Knowledge Discovery and Data Mining, 2007

524 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bd3068f787841e5af3041e1a89bfe9691630c87a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Jure Leskovec
2. Danai Koutra
3. Padraig Cunningham
4. Fosca Giannotti
5. Xiang Ren
6. Dónal Doyle
7. Dino Pedreschi
8. Huan Liu

In Statistical and Nonlinear Physics

1. Jure Leskovec
2. Huan Liu
3. Charu C. Aggarwal
4. Bryan Perozzi
5. Lei Tang
6. Punam Bedi
7. Chhavi Sharma
8. Yun Chi

Citations in the corpus

• Tracking the Evolution of Communities in Dynamic Social Networks (2010, background): “The strategy of finding the optimal match between communities in different time steps was previously considered in [16].”
• Dynamic Network Embedding by Modeling Triadic Closure Process (2018, background): “Meanwhile, there are many studies on identifying fundamental factors that cause network dynamics (Kossinets and Watts 2006; Tantipathananandh, Berger-Wolf, and Kempe 2007; Sun et al. 2007; Zhuang et al. 2013; Zhang et al. 2016).”
• Community Discovery in Dynamic Networks (2017, unknown): “…with a ground-truth or computing quality measures, they focus on the algorithmic complexity (Bourqui et al. , 2009; Rossetti et al. , 2016a; Tantipathananandh et al. , 2007), running time (Gupta et al. , 2012; Li et al. , 2011; Rossetti et al. , 2016a; Shao et al. , 2014), scalability…”
• Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (2014, background): “Recently, both link prediction and community detection have attracted significant attention and many efficient meth-ods have been proposed for different type of networks.”
• A classification for community discovery methods in complex networks (2011, methodology): “To sum up, the classical community discovery diffusionbased algorithms presented here are: a label propagation technique [38], dynamic node coloring for temporal evolving communities [39], and edge resistor algorithms that consider the original graph as an electric circuit [40].”
• Motifs in Temporal Networks (2016, methodology): “Existing methods either model the networks as strictly growing where a pair of nodes connect once and stay connected forever [2, 10, 17] or aggregate temporal information into a sequence of snapshots [1, 6, 23].”
• An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, background): “Tantipathananandh et al. [2007] have developed a framework for detecting dynamic community structure in evolving graphs.”
• Community Detection and Mining in Social Media (2010, background): “, 2009), graph coloring (Tantipathananandh et al., 2007), and a particle-and-density method (Kim and Han, 2009).”
• Anomaly detection in dynamic networks: a survey (2015, methodology): “Community based methods track the evolution of communities and their associated nodes in the graphs over time [77, 78, 79].”
• Community detection in social networks (2016, background): “Tantipathananandh et al. 101 made assumptions about the individual behavior and group membership.”
• Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, methodology): “Tantipathananandh et al. (2007) proposed an optimization-based approach for modeling dynamic community structure.”