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Date : 2017-10-26
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Individual and Collective Graph Mining Principles ~ About the Author Danai Koutra is an Assistant Professor in Computer Science and Engineering at University of Michigan Ann Arbor Her research interests include largescale graph mining graph similarity and matching graph summarization and anomaly detection
Individual and Collective Graph Mining Principles ~ Individual and Collective Graph Mining Principles Algorithms and Applications Synthesis Lectures on Data Mining and Knowledge Discovery by Danai Koutra Author Christos Faloutsos Author Jiawei Han Editor Lise Getoor Editor Wei Wang Editor 2 more
Individual and Collective Graph Mining Principles ~ Synthesis Lectures on Data Mining and Knowledge Discovery Graphs naturally represent information ranging from links between web pages to communication in email networks to connections between neurons in our brains These graphs often span billions of nodes and interactions between them
Individual and Collective Graph Mining Principles ~ We present applications of our exploration algorithms to massive datasets including a Web graph of 66 billion edges a Twitter graph of 18 billion edges brain graphs with up to 90 million edges collaboration peertopeer networks browser logs all spanning millions of users and interactions
Individual and Collective Graph Mining Principles ~ Fusing or aligning two or more networks is a fundamental building block of many graph mining tasks recommendation systems link prediction collective analysis of networks
Individual and collective graph mining principles ~ Individual and collective graph mining principles algorithms and applications Danai Koutra Christos Faloutsos Graphs naturally represent information ranging from links between web pages to communication in email networks to connections between neurons in our brains
Individual and Collective Graph Mining Morgan Claypool ~ Collective Graph Mining We extend the idea of individualgraph summarization to timeevolving graphs and show how to scalably discover temporal patterns Apart from summarization we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur temporal anomaly detection discovery of behavioral patterns and we present principled scalable algorithms for aligning networks and measuring their similarity
Individual and Collective Graph Mining Principles ~ Individual and Collective Graph Mining Principles Algorithms and Applications Synthesis Lectures on Data Mining and Knowledge Discovery Jiawei Han Lise Getoor Wei Wang Danai Koutra Christos Faloutsos ISBN 9781681732473 Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon
Book Review Individual and Collective Graph Mining ~ In the first part on Individual graph mining the authors have addressed two issues First is about the description of a large scale graph and second on what we deduce from the nodes In the second part on collective graph mining the authors provided a description of a large set of temporal graphs Further they measured the similarity between graphs
GitHub GemsLabMCbookIndividualCollectiveGraphMining ~ Individual and Collective Graph Mining Principles Algorithms and Applications Chapter 2 Summarization of Static Graphs VoG code Matlab Python Slides Chapter 3 Inference in a Graph Two Classes FaBP code Matlab Slides Multiple Classes mFaBP code Python mFaBP code SQL More detailed description derivations Full paper with all the proofs Slides Video
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