Graph Data Management: Techniques and Applications

502 Pages·2012·11.3 MB·English

by  Sherif Sakr, Sherif Sakr, Eric Pardede

Graph Data Management:
Techniques and Applications
Sherif Sakr
University of New South Wales, Australia
Eric Pardede
La Trobe University, Australia
Senior Editorial Director: Kristin Klinger
Director of Book Publications: Julia Mosemann
Editorial Director: Lindsay Johnston
Acquisitions Editor: Erika Carter
Development Editor: Mike Killian
Production Editor: Sean Woznicki
Typesetters: Adrienne Freeland, Jennifer Romanchak
Print Coordinator: Jamie Snavely
Cover Design: Nick Newcomer
Published in the United States of America by
Information Science Reference (an imprint of IGI Global)
701 E. Chocolate Avenue
Hershey PA 17033
Tel: [phone]
Fax: [phone]
E-mail: [email protected]
Web site: http://www.igi-global.com
Copyright © 2012 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in
any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher.
Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or
companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.
Library of Congress Cataloging-in-Publication Data
Graph data management: techniques and applications / Sherif Sakr and Eric Pardede, editors.
p. cm.
Includes bibliographical references and index.
Summary: “This book is a central reference source for different data management techniques for graph data structures and
their applications, discussing graphs for modeling complex structured and schemaless data from the Semantic Web, social
networks, protein networks, chemical compounds, and multimedia databases”--Provided by publisher.
ISBN 978-1-61350-053-8 (hardcover) -- ISBN 978-1-61350-054-5 (ebook) -- ISBN 978-1-61350-055-2 (print & perpetual
access) 1. Information visualization. 2. Graphic methods. I. Sakr, Sherif, 1979- II. Pardede, Eric, 1975-
QA276.3.G68 2012
001.4’226--dc23
2011021482
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the
authors, but not necessarily of the publisher.
Editorial Advisory Board
Sourav S. Bhowmick, Nanyang Technological University, Singapore
Michael Böhlen, University of Zurich, Switzerland
Marlon Dumas, University of Tartu, Estonia
Claudio Gutierrez, Universidad de Chile, Chile
Jun Huan, University of Kansas, USA
Irwin King, The Chinese University of Hong Kong, China
Raymond Wong, University of New South Wales, Australia
Mohammed Zaki, Rensselaer Polytechnic Institute, USA
Xiaofang Zhou, University of Queensland, Australia
Table of Contents
Foreword ..............................................................................................................................................vii
Preface ...................................................................................................................................................ix
Acknowledgment ...................................................................................................................................x
Section 1
Basic Challenges of Data Management in Graph Databases
Chapter 1
Graph Representation ..............................................................................................................................1
D. Dominguez-Sal, Universitat Politècnica de Catalunya, Spain
V. Muntés-Mulero, Universitat Politècnica de Catalunya, Spain
N. Martínez-Bazán, Universitat Politècnica de Catalunya, Spain
J. Larriba-Pey, Universitat Politècnica de Catalunya, Spain
Chapter 2
The Graph Traversal Pattern .................................................................................................................29
Marko A. Rodriguez, AT&T Interactive, USA
Peter Neubauer, Neo Technology, Sweden
Chapter 3
Data, Storage and Index Models for Graph Databases .........................................................................47
Srinath Srinivasa, International Institute of Information Technology, India
Chapter 4
An Overview of Graph Indexing and Querying Techniques ................................................................71
Sherif Sakr, University of New South Wales, Australia
Ghazi Al-Naymat, University of Tabuk, Saudi Arabia
Chapter 5
Efficient Techniques for Graph Searching and Biological Network Mining ........................................89
Alfredo Ferro, Università di Catania, Italy
Rosalba Giugno, Università di Catania, Italy
Alfredo Pulvirenti, Università di Catania, Italy
Dennis Shasha, Courant Institute of Mathematical Sciences, USA
Chapter 6
A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs ........................112
Jiefeng Cheng, The University of Hong Kong, China
Jeffrey Xu Yu, The Chinese University of Hong Kong, China
Chapter 7
Labelling-Scheme-Based Subgraph Query Processing on Graph Data ..............................................142
Hongzhi Wang, Harbin Institute of Technology, China
Jianzhong Li, Harbin Institute of Technology, China
Hong Gao, Harbin Institute of Technology, China
Section 2
Advanced Querying and Mining Aspects of Graph Databases
Chapter 8
G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases ..........................176
Xiaohong Wang, University of Kansas, USA
Jun Huan, University of Kansas, USA
Aaron Smalter, University of Kansas, USA
Gerald H. Lushington, University of Kansas, USA
Chapter 9
TEDI: Efficient Shortest Path Query Answering on Graphs ..............................................................214
Fang Wei, University of Freiburg, Germany
Chapter 10
Graph Mining Techniques: Focusing on Discriminating between Real and Synthetic Graphs ..........239
Ana Paula Appel, Federal University of Espirito Santo at São Mateus, Brazil
Christos Faloutsos, Carnegie Mellon University, USA
Caetano Traina Junior, University of São Paulo at São Carlos, Brazil
Chapter 11
Matrix Decomposition-Based Dimensionality Reduction on Graph Data .........................................260
Hiroto Saigo, Kyushu Institute of Technology, Japan
Koji Tsuda, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Chapter 12
Clustering Vertices in Weighted Graphs .............................................................................................285
Derry Tanti Wijaya, Carnegie Mellon University, USA
Stephane Bressan, National University of Singapore, Singapore
Chapter 13
Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks .............299
Charalampos E. Tsourakakis, Carnegie Mellon University, USA
Chapter 14
Graph Representation and Anonymization in Large Survey Rating Data ..........................................315
Xiaoxun Sun, Australian Council for Educational Research, Australia
Min Li, University of Southern Queensland, Australia
Section 3
Graph Database Applications in Various Domains
Chapter 15
Querying RDF Data ............................................................................................................................335
Faisal Alkhateeb, Yarmouk University, Jordan
Jérôme Euzenat, INRIA & LIG, France
Chapter 16
On the Efficiency of Querying and Storing RDF Documents ............................................................354
Maria-Esther Vidal, Universidad Simón Bolívar, Venezuela
Amadís Martínez, Universidad Simón Bolívar &Universidad de Carabobo, Venezuela
Edna Ruckhaus, Universidad Simón Bolívar, Venezuela
Tomas Lampo, University of Maryland, USA
Javier Sierra, Universidad Simón Bolívar, Venezuela
Chapter 17
Graph Applications in Chemoinformatics and Structural Bioinformatics ..........................................386
Eleanor Joyce Gardiner, University of Sheffield, UK
Chapter 18
Business Process Graphs: Similarity Search and Matching ...............................................................421
Remco Dijkman, Eindhoven University of Technology, The Netherlands
Marlon Dumas, University of Tartu, Estonia
Luciano García-Bañuelos, University of Tartu, Estonia
Chapter 19
A Graph-Based Approach for Semantic Process Model Discovery ....................................................438
Ahmed Gater, Universite de Versailles Saint-Quentin en Yvelines, France
Daniela Grigori, Universite de Versailles Saint-Quentin en Yvelines, France
Mokrane Bouzeghoub, Universite de Versailles Saint-Quentin en Yvelines, France
Chapter 20
Shortest Path in Transportation Network and Weighted Subdivisions ...............................................463
Radwa Elshawi, National ICT Australia (NICTA) - University of Sydney, Australia
Joachim Gudmundsson, National ICT Australia (NICTA) - University of Sydney, Australia
About the Contributors ....................................................................................................................475
Index ...................................................................................................................................................485
vii
Foreword
In recent years, many database researchers have become fascinated by graphs, of which I am one. Like
many others, I have spent years working with the relational model, including its data access methods,
optimizing techniques, and query languages. The rise of the graph model has been somewhat disruptive,
and our natural tendency is to ask, “Can we address new challenges using the existing relational model?”
Unfortunately, many efforts along this direction do not seem to work well.
The advent of the Web, and in particular large social networks on the Web, highlights the urgency of
developing a native graph database. The embracing of the graph model is anything but surprising. After
all, data management is about modeling the data and the relationships between the data. Can there be
a more natural model than the graph itself? The question, however, was not new. About three or four
decades ago, network and hierarchical models, which are also graph based, fought and lost the battle
against the relational model. Today, however, we are facing many new challenges that the relational
model is not designed for. For example, as graphs are becoming increasingly larger, storing a graph in
a relational table and using relational self-joins to traverse the graph are simply too costly.
Over the last decade, much research has been conducted on graphs. In particular, the study of large-
scale social networks has been made possible, and many interesting and even surprising results have been
published. However, the majority of research focuses on graph analytics using specific graph algorithms
(for example, graph reachability, sub-graph homomorphism and matching), and not enough effort has
been devoted to developing a new data model to better support graph analytics and applications on graphs.
This book is the first that approaches the challenges associated with graphs from a data management
point of view; it connects the dots. As I am currently involved in building a native graph database engine,
I encounter problems that arise from every possible aspect: data representation, indexing, transaction
support, parallel query processing, and many others. All of them sound familiar to a database researcher,
but the inherent change is fundamental as they originate from a new foundation. I found that this book
contains a lot of timely information, aiding my efforts. To be clear, it does not offer the blueprint for
building a graph database system, but it contains a bag of diamonds, enlightening the readers as they
start exploring a field that may fundamentally change data management in the future.
Haixun Wang
Microsoft Research Asia
viii
Haixun Wang, who earned a PhD in Computer Science from UCLA in 2000, joined Microsoft Re-
search Asia in 2009. Before then, he spent nine years as a research staff member at IBM Research Center,
where he was a technical assistant to Stuart Feldman, then-vice president of Computer Science, and to
Mark Wegman, head of Computer Science. Wang’s interest lies in data management and mining, and he
is working on building a large-scale knowledge base for advanced applications, including search. Wang
is on the editorial board of IEEE Transactions on Knowledge and Data Engineering, Knowledge and
Information Systems, and the Journal of Computer Science and Technology. He has held key leadership
roles in various top conferences in his field
ix
Preface
The graph is a powerful tool for representing and understanding objects and their relationships in vari-
ous application domains. Recently, graphs have been widely used to model many complex structured
and schemaless data such as semantic web, social networks, biological networks, protein networks,
chemical compounds and business process models. The growing popularity of graph databases has
generated interesting data management problems. Therefore, the domain of graph databases have at-
tracted a lot of attention from the research community and different challenges have been discussed such
as: subgraph search queries, supergraph search queries, approximate subgraph matching, short path
queries and graph mining techniques.
This book is designed for studying various fundamental challenges of storing and querying graph
databases. In addition, it discusses the applications of graph databases in various domains. In particular,
the book is divided into three main sections.
The first section discusses the basic definitions of graph data models, graph representations and graph
traversal patterns. It also provides an overview of different graph indexing techniques and evaluation
mechanisms for the main types of graph queries. The second section further discusses advanced query-
ing aspects of graph databases and different mining techniques of graph databases. It should be noted
that many graph querying algorithms are sensitive to the application scenario in which they are designed
and cannot be generalized for all domains. Therefore, the third section focuses on presenting the usage
of graph database techniques in different practical domains such as: semantic web, chemoinformatics,
bioinformatics, business process model and transportation networks.
In a nutshell, the book provides a comprehensive summary from both of the algorithmic and the
applied perspectives. It will provide the reader with a better understanding of how graph databases can
be effectively utilized in different scenarios.
Sherif Sakr
University of New South Wales, Australia
Eric Pardede
La Trobe University, Australia