A framework for analysis of dynamic social networks
By Tanya Y. Berger-Wolf, Jared Saia. Knowledge Discovery and Data Mining, 2006
298 citations recorded by the source corpus.
Subfields: Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/fc98e3bf23d1dc694f6753ebf29eb1d984d0d143
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Sociology and Political Science
- David Kempe
- Chayant Tantipathananandh
- Tanya Y. Berger-Wolf
- Fosca Giannotti
- Dino Pedreschi
- Mudhakar Srivatsa
- Carl A. Gunter
- Michele Coscia
In Statistical and Nonlinear Physics
- Punam Bedi
- Chhavi Sharma
- Fosca Giannotti
- Dino Pedreschi
- Michele Coscia
- Chayant Tantipathananandh
- David Kempe
- Tanya Y. Berger-Wolf
Citations in the corpus
- Microscopic Social Influence (2012, background): “Existing research mostly focuses on studying network dynamics within the setting of a single, homogenous network, with examples including diffusion and propagation [1], influence maximization [2, 7], community formation [16], and network evolution [17, 18].”
- The dynamics of animal social networks: analytical, conceptual, and theoretical advances (2014, unknown): “The second approach for studying network dynamics is the continuous approach that maintains data in streams of time-stamped observations (Bender-deMoll and McFarland 2006; Berger-Wolf and Saia 2006; Palla et al. 2007; Tantipathananandh and BergerWolf 2009; Blonder and Dornhaus 2011; Blonder et al. ”
- Community detection in social networks (2016, background): “Wolf et al. 100 proposed mathematical and computational formulations for the analysis of dynamic communities on the basis of social interactions occurring in the network.”
- You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020, background): “Then PROVDETECTOR can model the path selection problem as an incremental K longest paths problem on a dynamic graph, which is an orthogonal problem and has existing solutions [56], [57].”
- A classification for community discovery methods in complex networks (2011, background): “approach, which represents an evolution of [137], the base input representation is an evolving bipartite graph of individuals connected to events.”
- A framework for community identification in dynamic social networks (2007, methodology): “[4] T. Y. Berger-Wolf and J. Saia.”