PPT-Approximate Graph Matching

R Srikant ECECSL UIUC Coauthor Joseph Lubars Problem Statement Given two correlated graphs One with known node identities One with unknown or incorrect node identities Goal Infer the identities of the nodes in the second graph. fzhoucom ftorrecscmuedu Abstract Graph matching GM is a fundamental problem in com puter science and it has been successfully applied to many problems in computer vision Although widely used exist ing GM algorithms cannot incorporate global consisten Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. Based on. http://www.cs.engr.uky.edu/~. lewis/cs-heuristic/text/integer/linprog.html. The . bipartite graph matching problem.  is to find a set of unconnected edges which cover as many of the vertices as possible. If we select the set of edges. Christopher . Ré. and Dan . Suciu. University of Washington. Approximate Lineage in One Slide. Lineage (Provenance) . In QP used to track correlations. Explain query/view results. VLDBs have lots of lineage. Add fill edge a . ->. b if there is a path from a to b through lower-numbered vertices.. But this doesn. ’. t work with numerical pivoting!. 1. 2. 3. 4. 7. 6. 5. A. G (A) . L+U. Nonsymmetric Gaussian elimination. Arijit Khan, . Yinghui. Wu, Xifeng Yan. Department of Computer Science. University of California, Santa Barbara. {. arijitkhan. , . yinghui. , . xyan. }@. cs.ucsb.edu. Graph Data. 2. Graphs are everywhere.. Subgraph. Matching on Large Graphs in Cloud. Zhao Chang*, Lei . Zou. †, . Feifei. Li*. *University of Utah, USA . †. Peking University, China. Outline. Background. K-. Automorphism. Subgraph. Other Graph Algorithms. Bipartite graph and Bipartite matching. Bipartite graph. Divide into two groups, A and B. All edges are from something in group A to something in group B. Bipartite matching. Want to uniquely match one item from group A with one item in group B. CS302, Spring . 2013 . David Kauchak. Admin. CS lunch today. Grading. Flow graph/networks. S. A. B. T. 20. 20. 1. 0. 1. 0. 30. Flow network. directed, weighted graph (V, E). positive edge . NIbble. 2. Why I’m talking about graphs. Lots of large data . is . graphs. Facebook, Twitter, citation data, and other . social. networks. The web, the blogosphere, the semantic web, Freebase, . W. Olivier . Duchenne. , Francis Bach, . Inso. . Kweon. , Jean Ponce. École. . Normale. . Supérieure. , INRIA, KAIST. Team Willow. Extension of [. Leordeanu. &. Hebert]. to the case of . hypergraph.. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box. .: . A. A. A. A. A. A. A. A. A. A. Topics. Solving Integer Programs. Basic Combinatorial Optimization Problems. Rashid Kaleem. 1. , . Anand. Venkat. 2. , . Sreepathi. Pai. 1. , Mary Hall. 2. , . Keshav. Pingali. 1. 1. University . of Texas at Austin. 2. University of Utah. Outline. Stochastic Gradient Descent. 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.