PDF-Connection Subgraphs in Social Networks Christos Faloutsos Kevin S

McCurley Andrew Tomkins Abstract connection subgraph is a small ie viewable subgraph of a social network that best captures the relationship between two people We present a formal de64257nition of this problem and an ideal solution based on computa. cmuedu wangbaibupteducn ABSTRACT Given a real and weighted persontoperson network which changes over time what can we say about the cliques that it contains Do the incidents of communication or weights on the edges of a clique follow any pattern Real We present a general methodology for near optimal sensor placement in these and related problems We demonstrate that many realistic outbreak detection objectives eg de tection likelihood population a64256ected exhibit the prop erty of submodularity B. Aditya Prakash. Computer Science. Virginia Tech.. GraphEx. . Symposium, MIT Endicott House, Aug 21, 2014 . Thanks!. Ali Pinar. Ben Miller. Prakash 2014. 2. Networks are everywhere!. Human Disease Network [. B. Aditya . Prakash . Virginia Tech. Jilles. . Vreeken. . University of Antwerp. Christos . Faloutsos. . Carnegie Mellon University. IEEE ICDM Brussels. December 11, 2012. Contagions. Social collaboration. Radu Curticapean, Holger Dell, Dániel Marx. NEW INSIGHTS INTO. Saarland University,. Cluster . of. Excellence (MMCI). Institute . for. Computer Science . and. Control,. Hungarian. Academy . of. . Commissioning status. Christos Zamantzas on behalf of the BLM team.. Machine Protection Panel 17/04/2015. Overview . Status of checks performed during:. Hardware Commissioning. Machine Checkout. Beam Tests. Christos Faloutsos. CMU. Thanks. Alex . Smola. Jia. Yu (Tim) Pan. Google, June 2013. C. Faloutsos (CMU). 2. C. Faloutsos (CMU). 3. Roadmap. Graph problems:. G1: Fraud detection – BP. G2: . Botnet. B. Aditya Prakash. Computer Science. Virginia Tech.. GraphEx. . Symposium, MIT Endicott House, Aug 21, 2014 . Thanks!. Ali Pinar. Ben Miller. Prakash 2014. 2. Networks are everywhere!. Human Disease Network [. Graphs:. Patterns . and Algorithms. Christos Faloutsos. CMU. Thank you!. Dr. Ching-Hao (Eric) Mao. Prof. Kenneth Pao. Taiwan, Aug'12. C. Faloutsos (CMU). 2. C. Faloutsos (CMU). 3. Our goal:. Open source system for mining huge graphs:. Mitnick. Famous Social Engineer Hacker. Went to prison for hacking. Became ethical hacker. "People are generally helpful, especially to someone who is nice, knowledgeable or insistent.". Kevin . Mitnick. Christos Faloutsos. CMU. C. Faloutsos (CMU). 2. Roadmap. Introduction – . Motivation. Why study (big) graphs?. Part#. 1: Patterns in graphs. Part#2: Cascade analysis. Conclusions. [Extra: ebay fraud; tensors; spikes]. B. . Aditya. . Prakash. Carnegie Mellon University.  . Virginia Tech. . Christos Faloutsos. Carnegie Mellon University. Aug 29, Tutorial, VLDB 2012, Istanbul. Networks are everywhere!. Human Disease Network [. B. . Aditya. . Prakash. Carnegie Mellon University.  . Virginia Tech. . Christos Faloutsos. Carnegie Mellon University. Aug 29, Tutorial, VLDB 2012, Istanbul. Networks are everywhere!. Human Disease Network [. B. . Aditya. . Prakash. http://www.cs.cmu.edu/~badityap. Christos . Faloutsos. http://www.cs.cmu.edu/~christos. Carnegie Mellon University. Networks are everywhere!. Human Disease Network [. Barabasi.