PPT-Real-Time Multi-Criteria Social Graph Partitioning:

A Game Theoretic Approach Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data pp 16171628 20160512 M1 kosaka Author Nikos Armenatzoglou PhD at the Department of Computer Science and Engineering Hong Kong University of Science and . Wei Wang. Department of Computer Science. Scalable Analytics Institute. UCLA. weiwang@cs.ucla.edu. Graphs/Networks. FFSM (ICDM03), SPIN (KDD04),. GDIndex. (ICDE07). MotifMining. (PSB04, RECOMB04, ProteinScience06, SSDBM07, BIBM08). : . Multi-criterion representation . for scene . understanding. Moos . Hueting. ∗ . Aron. . Monszpart. ∗ . Nicolas . Mellado. University College London. https://www.imagestore.ucl.ac.uk/home/pcache/10002/aa/black_ad80e.jpg. Isabelle Stanton, UC Berkeley. Gabriel . Kliot. , Microsoft Research XCG. Modern graph datasets are huge. The web graph had over a trillion links in 2011. Now?. . facebook. has “more than 901 million users with average degree 130”. Graphs: An Approach to Modeling Networks. Jure . Leskovec. , . Deepayan. . Chakrabarti. , Jon Kleinberg, Christos . Faloutsos. , . Zoubin. . Ghahramani. Presented by Eric Wang. 4/21/2011. Introduction. Chris Kang. shinwook@uw.edu. University of Washington . NESSIS 2015. Motivation. Objective of a coach is to “win a hockey game” by:. Finding “chemistry” between players. Finding “balance” among the lines (allocating appropriate Time on Ice). Prasun Dewan. Department of Computer Science. University of North Carolina at Chapel Hill. dewan@cs.unc.edu. COLLABORATIVE APPLICATION. U1. U2. U3. Application. User inputs “draw red circle”. Coupling. Vaibhav. . Mallya. EECS 767. D. . Radev. 1. Agenda. Agenda. Basic Definitions. Girvan-Newman Algorithm. Donetti. -Munoz Spectral Method. Karypis. -Kumar Multi-level Partitioning. Graclus. GraphClust. a Multi-Layered Indexing Approach. Yongjiang Liang, . Peixiang Zhao. CS @ FSU. zhao@cs.fsu.edu. Outline. Introduction. State-of-the-art solutions. ML-Index & similarity search. Experiments. Conclusion. Graph Partitioning (cuts, spectral clustering, density), . Community evolution. 1. Introduction. modules, cluster, communities, groups, . partitions (more on this today). 2. Summary of Part I. PART . (in-depth analysis of research literature). 1. Scalable SPARQL Querying of Large RDF Graphs . (aug-sep 2011). 2. Towards Effective Partition Management for Large Graphs . (may 2012). Vineet Choudhury. Goals. Enhance the . social experience of. an event. Social networking. Integrate. D. ata . from. social . networking . systems. S. emantic . data sources. . Collaboration networks . Communities . Distributed Graph Analytics. Loc Hoang               Roshan Dathathri. Gurbinder Gill                Keshav Pingali. 1. Distributed Graph Analytics. Analytics on unstructured data. Finding suspicious actors in crime networks. Multi-Cores . With Graph Awareness. Vijayan, Ming, Xuetian, Frank, Lidong, Maya. Microsoft Research. Motivation. Tremendous increase in graph data and applications. New class of graph applications that require real-time responses. William Cohen. Outline. Last week:. PageRank – one sample algorithm on graphs. edges and nodes in memory. nodes in memory. nothing in memory. Aapo. Properties of (social) graphs. GraphLab. /. Powergraph.