PPT-Node Similarity, Graph Similarity and Matching:

Theory and Applications Danai Koutra CMU Tina EliassiRad Rutgers Christos Faloutsos CMU SDM 2014 Friday April 25 th 2014 Philadelphia PA Who we are Danai Koutra CMU Node and graph similarity. Given:. A query image. A database of images with known locations. Two types of approaches:. Direct matching. : directly match image features to 3D points (high memory requirement). Retrieval based. : retrieve a short list of most similar images and perform image matching. -. based Clustering. Mohammad. . Rezaei. , Pasi Fränti. rezaei@cs.uef.fi. Speech. and . Image. . Processing. . Unit. University of Eastern Finland. . August 2014. Keyword-Based Clustering. An object such as a text document, website, movie and service can be described by a set of keywords. from . GOMMA. Michael . Hartung. , Lars Kolb, . Anika. . Groß. , Erhard Rahm. Database . Research Group. University of . Leipzig. 9th . Intl. . . Conf. . on Data Integration. in . the. Life . Sciences. WordNet. Lubomir. . Stanchev. Example . Similarity Graph. Dog. Cat. 0.3. 0.3. Animal. 0.8. 0.2. 0.8. 0.2. Applications. If we type . automobile. . in our favorite Internet search engine, for example Google or Bing, then all top results will contain the word . Synthetic Chemical Compounds. Application to Metabolomics. Mai . Hamdalla. , David Grant, Ion . Mandoiu. , Dennis Hill, . Sanguthevar. . Rajasekaran. and . Reda. . Ammar. University of Connecticut. these theories have explanatory power domains partially role of relational judgments. Previous structural and aspects of notion of relational similarity by the fact that and her some ways there is in AHMED K. ELMAGARMID . PURDUE UNIVERSITY, WEST LAFAYETTE, . IN. Senior member, IEEE. PANAGIOTIS G. IPEIROTIS . LEONARD N. STERN SCHOOL OF BUSINESS, NEW YORK, . NY . Member, IEEE computer security. VASSILIOS S. VERYKIOS. 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.. 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. CS159 . Fall . 2014. Admin. Assignment 4. Quiz #2 Thursday. Same . rules as quiz #1. First 30 minutes of class. Open book and . notes. Assignment 5 out on Thursday. Quiz #2. Topics. Linguistics 101. Parsing. R. Srikant. ECE/CSL. 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. Biomedical Publications. Comparing the Accuracies of Nine . Text-Based . Similarity Approaches. Boyack et al. (2011). . PLoS. ONE 6(3): e18029. Motivation . Compare different similarity measurements. Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. Li, Mark Drew. School of Computing Science, . Simon . Fraser University, . Vancouver. , B.C., Canada. {zza27, . li. , mark}@. cs.sfu.ca. Learning Image Similarities via Probabilistic Feature Matching.