Sparsification–A Technique for Speeding up Dynamic Graph Algorithms
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Knowledge Spaces Applications in Education Knowledge Spaces
Jean-Claude Falmagne · Dietrich Albert Christopher Doble · David Eppstein Xiangen Hu Editors Knowledge Spaces Applications in Education Knowledge Spaces Jean-Claude Falmagne • Dietrich Albert Christopher Doble • David Eppstein • Xiangen Hu Editors Knowledge Spaces Applications in Education Editors Jean-Claude Falmagne Dietrich Albert School of Social Sciences, Department of Psychology Dept. Cognitive Sciences University of Graz University of California, Irvine Graz, Austria Irvine, CA, USA Christopher Doble David Eppstein ALEKS Corporation Donald Bren School of Information Irvine, CA, USA & Computer Sciences University of California, Irvine Irvine, CA, USA Xiangen Hu Department of Psychology University of Memphis Memphis, TN, USA ISBN 978-3-642-35328-4 ISBN 978-3-642-35329-1 (eBook) DOI 10.1007/978-3-642-35329-1 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013942001 © Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. -
Tarjan Transcript Final with Timestamps
A.M. Turing Award Oral History Interview with Robert (Bob) Endre Tarjan by Roy Levin San Mateo, California July 12, 2017 Levin: My name is Roy Levin. Today is July 12th, 2017, and I’m in San Mateo, California at the home of Robert Tarjan, where I’ll be interviewing him for the ACM Turing Award Winners project. Good afternoon, Bob, and thanks for spending the time to talk to me today. Tarjan: You’re welcome. Levin: I’d like to start by talking about your early technical interests and where they came from. When do you first recall being interested in what we might call technical things? Tarjan: Well, the first thing I would say in that direction is my mom took me to the public library in Pomona, where I grew up, which opened up a huge world to me. I started reading science fiction books and stories. Originally, I wanted to be the first person on Mars, that was what I was thinking, and I got interested in astronomy, started reading a lot of science stuff. I got to junior high school and I had an amazing math teacher. His name was Mr. Wall. I had him two years, in the eighth and ninth grade. He was teaching the New Math to us before there was such a thing as “New Math.” He taught us Peano’s axioms and things like that. It was a wonderful thing for a kid like me who was really excited about science and mathematics and so on. The other thing that happened was I discovered Scientific American in the public library and started reading Martin Gardner’s columns on mathematical games and was completely fascinated. -
Drawing Graphs and Maps with Curves
Report from Dagstuhl Seminar 13151 Drawing Graphs and Maps with Curves Edited by Stephen Kobourov1, Martin Nöllenburg2, and Monique Teillaud3 1 University of Arizona – Tucson, US, [email protected] 2 KIT – Karlsruhe Institute of Technology, DE, [email protected] 3 INRIA Sophia Antipolis – Méditerranée, FR, [email protected] Abstract This report documents the program and the outcomes of Dagstuhl Seminar 13151 “Drawing Graphs and Maps with Curves”. The seminar brought together 34 researchers from different areas such as graph drawing, information visualization, computational geometry, and cartography. During the seminar we started with seven overview talks on the use of curves in the different communities represented in the seminar. Abstracts of these talks are collected in this report. Six working groups formed around open research problems related to the seminar topic and we report about their findings. Finally, the seminar was accompanied by the art exhibition Bending Reality: Where Arc and Science Meet with 40 exhibits contributed by the seminar participants. Seminar 07.–12. April, 2013 – www.dagstuhl.de/13151 1998 ACM Subject Classification I.3.5 Computational Geometry and Object Modeling, G.2.2 Graph Theory, F.2.2 Nonnumerical Algorithms and Problems Keywords and phrases graph drawing, information visualization, computational cartography, computational geometry Digital Object Identifier 10.4230/DagRep.3.4.34 Edited in cooperation with Benjamin Niedermann 1 Executive Summary Stephen Kobourov Martin Nöllenburg Monique Teillaud License Creative Commons BY 3.0 Unported license © Stephen Kobourov, Martin Nöllenburg, and Monique Teillaud Graphs and networks, maps and schematic map representations are frequently used in many fields of science, humanities and the arts. -
The Best Nurturers in Computer Science Research
The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities. -
Lecture Notes in Computer Science
D. Dolev Z. Galil M. Rodeh (Eds.) Theory of Computing and Systems ISTCS '92, Israel Symposium Haifa, Israel, May 27-28, 1992 Proceedings Springer-Verlag Berlin Heidelberg NewYork London Paris Tokyo Hong Kong Barcelona Budapest Series Editors Gerhard Goos Juris Hartmanis Universit~it Karlsruhe Department of Computer Science Postfach 69 80 Cornell University Vincenz-Priessnitz-Stral3e 1 5149 Upson Hall W-7500 Karlsruhe, FRG Ithaca, NY 14853, USA Volume Editors Danny Dolev Hebrew University Givat Ram, Jerusalem, Israel Zvi Galil Columbia University, New York, NY 10027, USA and Tel Aviv University Ramat Aviv, Tel Aviv, Israel Michael Rodeh IBM Israel Ltd., Science and Technology, Technion City Haifa 32000, Israel CR Subject Classification (1991): F.1-4, B.3, C.2, G.1-2, 1.1, E.4, D.2-3 ISBN 3-540-55553-6 Springer-Verlag Berlin Heidelberg New York ISBN 0-387-55553-6 Springer-Verlag New York Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag Berlin Heidelberg 1992 Printed in Germany Typesetting: Camera ready by author/editor Printing and binding: Druckhaus Beltz, Hemsbach/Bergstr. -
Forbidden Configurations in Discrete Geometry
FORBIDDEN CONFIGURATIONS IN DISCRETE GEOMETRY This book surveys the mathematical and computational properties of finite sets of points in the plane, covering recent breakthroughs on important problems in discrete geometry and listing many open prob- lems. It unifies these mathematical and computational views using for- bidden configurations, which are patterns that cannot appear in sets with a given property, and explores the implications of this unified view. Written with minimal prerequisites and featuring plenty of fig- ures, this engaging book will be of interest to undergraduate students and researchers in mathematics and computer science. Most topics are introduced with a related puzzle or brain-teaser. The topics range from abstract issues of collinearity, convexity, and general position to more applied areas including robust statistical estimation and network visualization, with connections to related areas of mathe- matics including number theory, graph theory, and the theory of per- mutation patterns. Pseudocode is included for many algorithms that compute properties of point sets. David Eppstein is Chancellor’s Professor of Computer Science at the University of California, Irvine. He has more than 350 publications on subjects including discrete and computational geometry, graph the- ory, graph algorithms, data structures, robust statistics, social network analysis and visualization, mesh generation, biosequence comparison, exponential algorithms, and recreational mathematics. He has been the moderator for data structures and algorithms -
Graphs in Nature
Graphs in Nature David Eppstein University of California, Irvine Symposium on Geometry Processing, July 2019 Inspiration: Steinitz's theorem Purely combinatorial characterization of geometric objects: Graphs of convex polyhedra are exactly the 3-vertex-connected planar graphs Image: Kluka [2006] Overview Cracked surfaces, bubble foams, and crumpled paper also form natural graph-like structures What properties do these graphs have? How can we recognize and synthesize them? I. Cracks and Needles Motorcycle graphs: Canonical quad mesh partitioning Paper at SGP'08 [Eppstein et al. 2008] Problem: partition irregular quad-mesh into regular submeshes Inspiration: Light cycle game from TRON movies Mesh partitioning method Grow cut paths outwards from each irregular (non-degree-4) vertex Cut paths continue straight across regular (degree-4) vertices They stop when they run into another path Result: approximation to optimal partition (exact optimum is NP-complete) Mesh-free motorcycle graphs Earlier... Motorcycles move from initial points with given velocities When they hit trails of other motorcycles, they crash [Eppstein and Erickson 1999] Application of mesh-free motorcycle graphs Initially: A simplified model of the inward movement of reflex vertices in straight skeletons, a rectilinear variant of medial axes with applications including building roof construction, folding and cutting problems, surface interpolation, geographic analysis, and mesh construction Later: Subroutine for constructing straight skeletons of simple polygons [Cheng and Vigneron 2007; Huber and Held 2012] Image: Huber [2012] Construction of mesh-free motorcycle graphs Main ideas: Define asymmetric distance: Time when one motorcycle would crash into another's trail Repeatedly find closest pair and eliminate crashed motorcycle Image: Dancede [2011] O(n17=11+) [Eppstein and Erickson 1999] Improved to O(n4=3+) [Vigneron and Yan 2014] Additional log speedup using mutual nearest neighbors instead of closest pairs [Mamano et al. -
Efficient Algorithms for Finding Maximum Matching in Graphs
Efficient Algorithms for Finding Maximum Matching in Graphs ZVI GALIL Department of Computer Science, Columbia University, New York, N. Y., 10027 and Tel-Aviv University, Tel-Aviv, Israel This paper surveys the techniques used for designing the most efficient algorithms for finding a maximum cardinality or weighted matching in (general or bipartite) graphs. It also lists some open problems concerning possible improvements in existing algorithms and the existence of fast parallel algorithms for these problems. Categories and Subject Descriptors: F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems-computation on discrete structures; G.2.2 [Discrete Mathematics]: Graph Theory-graph algorithms General Terms: Algorithms, Theory, Verification Additional Key Words and Phrases: Algorithmic tools, the asexual case, assignment problem, augmenting path, blossoms, data structures, d-heap, duality, ET, generalized priority queue, Main Theorem of Botany, matching, monsters, moonlighting, polygamy, primal-dual method, shmathematics, shrink, warm-up INTRODUCTION tools are “divide and conquer” and dynamic There are no recipes for designing efficient programming [Aho et al. 19741. Alterna- algorithms. This is somewhat unfortunate tively, we may try to find a way to reduce from the point of view of applications: Any the number of steps in the original algo- time we have to design an algorithm, we rithm by finding a better way to organize may have to start (almost) from scratch. the information. However, it is fortunate from the point of (c) Data Structures. Sometimes we can view of researchers: It is unlikely that we speed up an algorithm by using an efficient are going to run out of problems or chal- data structure that supports the primitive lenges. -
Giuseppe F. Italiano
Giuseppe F. Italiano Dipartimento di Ingegneria Civile e Ingegneria Informatica +39-06-72597394 Università di Roma “Tor Vergata” Fax +39-06-72597460 via del Politecnico 1 [email protected] 00133 Roma, Italy http://people.uniroma2.it/giuseppe.italiano/ RESEARCH INTERESTS My interests are in the design, analysis, implementation and experimental evaluation of algorithms and data structures. My research areas include algorithm engineering, combinatorial algorithms, computer security, graph algorithms and string algorithms. PERSONAL Born on March 16, 1961 in Italy. Italian Citizen. Married plus four. Enjoy running. EDUCATION 1988 - 91 Ph.D. in Computer Science. Columbia University, New York, NY. Thesis Advisor: Prof. Zvi Galil. Thesis Title: “Dynamic data structures for graphs”. 1990 M.Phil. Columbia University, New York, NY. 1987 - 88 M.Sc. in Computer Science. Columbia University, New York, NY. 1979 - 85 Laurea summa cum laude in Electrical Engineering. University of Rome “La Sapienza”, Roma, Italy. Thesis Advisor: Prof. Giorgio Ausiello. EMPLOYMENT 1998 - Professor of Computer Science. University of Rome “Tor Vergata”, Italy. 2004 - 12 Chair of the Department of Computer Science, Systems and Production. University of Rome “Tor Vergata”. 1995 - 98 Professor of Computer Science. University of Venice “Ca’ Foscari”, Italy. 1994 - 95 Professor of Computer Science. University of Salerno, Italy. 1991 - 95 Research Staff Member. IBM Research Division, T.J. Watson Research Center, Com- munication Systems Department. 1989 - 94 Assistant Professor. University of Rome “La Sapienza”, Italy. OTHER APPOINTMENTS 2017 - Panel Member for Mathematics, Statistics and Computer Science. Danish Council for Independent Research (DFF). 2013 - Vice-Rector for Quality, Evaluation and Performance. University of Rome “Tor Ver- gata". -
Dynamic Generators of Topologically Embedded Graphs David Eppstein
Dynamic Generators of Topologically Embedded Graphs David Eppstein Univ. of California, Irvine School of Information and Computer Science Dynamic generators of topologically embedded graphs D. Eppstein, UC Irvine, SODA 2003 Outline New results and related work Review of topological graph theory Solution technique: tree-cotree decomposition Dynamic generators of topologically embedded graphs D. Eppstein, UC Irvine, SODA 2003 Outline New results and related work Review of topological graph theory Solution technique: tree-cotree decomposition Dynamic generators of topologically embedded graphs D. Eppstein, UC Irvine, SODA 2003 New Results Given cellular embedding of graph on surface, construct and maintain generators of fundamental group Speed up other dynamic graph algorithms for topologically embedded graphs (connectivity, MST) Improve constant in separator theorem for low-genus graphs Construct low-treewidth tree-decompositions of low-genus low-diameter graphs Dynamic generators of topologically embedded graphs D. Eppstein, UC Irvine, SODA 2003 New Results: Fundamental Group Generators Fundamental group is formed by loops on surface Provides important topological information about surface, used as basis for all our other algorithms Can be described by a system of generators (independent loops) and relations (concatenations of loops that bound disks) Time to construct this system: O(n) Related work: Canonical schema of Vegter and Yap [SoCG 90] (set of generators satisfying prespecified relations) Time to construct canonical schema: O(gn) -
Listing K-Cliques in Sparse Real-World Graphs Maximilien Danisch, Oana Balalau, Mauro Sozio
Listing k-cliques in Sparse Real-World Graphs Maximilien Danisch, Oana Balalau, Mauro Sozio To cite this version: Maximilien Danisch, Oana Balalau, Mauro Sozio. Listing k-cliques in Sparse Real-World Graphs. 2018 World Wide Web Conference, Apr 2018, Lyon, France. pp.589-598, 10.1145/3178876.3186125. hal-02085353 HAL Id: hal-02085353 https://hal.archives-ouvertes.fr/hal-02085353 Submitted on 8 Apr 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Listing k-cliques in Sparse Real-World Graphs∗ Maximilien Danischy Oana Balalauz Mauro Sozio Sorbonne Université, CNRS, Max Planck Institute for Informatics, LTCI, Télécom ParisTech University Laboratoire d’Informatique de Paris 6, Saarbrücken, Germany Paris, France LIP6, F-75005 Paris, France [email protected] [email protected] [email protected] ABSTRACT of edges [36, 40, 49] within a few hours. In contrast, listing all k- Motivated by recent studies in the data mining community which cliques is often deemed not feasible with most of the works focusing require to efficiently list all k-cliques, we revisit the iconic algorithm on approximately counting cliques [31, 42]. of Chiba and Nishizeki and develop the most efficient parallel algo- Recent works in the data mining and database community call rithm for such a problem. -
R, 44, 177 Abu-Khzam, Faisal N., 51 Agarwal, Pankaj K., 61, 190
Cambridge University Press 978-1-108-42391-5 — Forbidden Configurations in Discrete Geometry David Eppstein Index More Information Index ∃R, 44, 177 Balko, Martin, 10 Balogh, Jozsef, 93 Abu-Khzam, Faisal N., 51 Bannister, Michael J., 151, 179, 181, Agarwal, Pankaj K., 61, 190 182 Aichholzer, Oswin, 10, 34, 131 Baran, Ilya, 60 Ailon, Nir, 59 Barnett, Vic, 129 algorithm, 33 Beck, József, 89 Alimonti, Paola, 45 betweenness, 14 Alon, Noga, 71 Bézout’s theorem, 74 alphabet, 88 binary relation, 20 Anderson, David Brent, 73 binary search, 139 Anning, Norman H., 142 binomial, 107–109 anti-symmetry, 20 binomial coefficient, 2, 107 antichain, 21, 22, 66, 123, 128, 135, bipartite graph, 168, 173–175 175, 201 bitangent, 16, 17 antimatroid, 164 Björner, Anders, 11 Apollonian network, 185, 186 Borwein, Peter, 53 Appel, Kenneth, 45 bounded obstacles, 25 approximation ratio, 45, 67, 68, 70, 71, bounding box, 198 75, 81–83, 98, 100, 118, 119, 130, Brahmagupta, 142 172, 196, 197, 209, 210 Brandenburg, Franz J., 181, 182 APX, 45, 67, 75, 119, 172, 209 Bremner, David, 136 area, 35 Brinkmann, Gunnar, 184 Arkin, Esther M., 106, 117, 118, brute force search, 44, 51, 62, 77, 172, 130 184 Aronov, Boris, 128 Bukh, Boris, 151, 157 arrangement of lines, 59, 62, 116, Burr, Michael A., 140 177–179, 191, 193 Burr, Stefan A., 54, 55 arrow symbol, 88, 90 Aurenhammer, Franz, 10 Cabello, Sergio, 184 avoids, 27–29, 31, 39, 48, 49, 52, 55, cap, 108 72, 75, 106, 119, 120, 126, 171, cap-set, 91, 92 172 Carathéodory, Constantin, 106 223 © in this web service Cambridge University