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An Event-based Framework for Characterizing the Evolutionary Behavior of Interaction Graphs
Published on Aug 14, 20075374 Views
Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. H
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Chapter list
An Event-based Framework for Characterizing the Evolutionary Behavior of Interaction Graphs00:00
Motivation-part0100:16
Motivation-part0201:04
Motivation-part0301:35
Motivation-part0401:57
Workflow02:28
Temporal Snapshots02:55
Clustering 03:45
Community-based Event Detection04:50
Entity-based Event Detection05:52
Event Detection06:53
Temporal Analysis07:35
Behavioral Analysis08:19
Case Study 1 : DBLP Collaboration network08:51
Case Study 2 : Clinical Trials Network09:25
Stability Index10:35
Stability for Clinical Trials data 11:28
Sociability Index12:41
Sociability Index for Community Prediction13:22
Experimental Results13:57
Popularity Index15:00
Application of Popularity Index15:24
Influence Index15:52
Top Influential authors – DBLP dataset16:50
Diffusion Models17:02
Diffusion Models – Influence Maximization17:36
Conclusions18:29
Future Directions19:11
Thanks!19:31