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KDD
2008
ACM
224views Data Mining» more  KDD 2008»
16 years 6 months ago
The structure of information pathways in a social communication network
Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dyn...
Gueorgi Kossinets, Jon M. Kleinberg, Duncan J. Wat...
KDD
2007
ACM
184views Data Mining» more  KDD 2007»
16 years 6 months ago
GraphScope: parameter-free mining of large time-evolving graphs
How can we find communities in dynamic networks of social interactions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity timepoints in ...
Jimeng Sun, Christos Faloutsos, Spiros Papadimitri...
KDD
2007
ACM
220views Data Mining» more  KDD 2007»
16 years 6 months ago
SCAN: a structural clustering algorithm for networks
Network clustering (or graph partitioning) is an important task for the discovery of underlying structures in networks. Many algorithms find clusters by maximizing the number of i...
Xiaowei Xu, Nurcan Yuruk, Zhidan Feng, Thomas A. J...
KDD
2006
ACM
146views Data Mining» more  KDD 2006»
16 years 6 months ago
Structure and evolution of online social networks
In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess ...
Ravi Kumar, Jasmine Novak, Andrew Tomkins
CHI
2005
ACM
16 years 6 months ago
Understanding research trends in conferences using paperLens
PaperLens is a novel visualization that reveals trends, connections, and activity throughout a conference community. It tightly couples views across papers, authors, and reference...
Bongshin Lee, Mary Czerwinski, George G. Robertson...