A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Data on the Web: from relations to semistructured data and XML
Data on the Web: from relations to semistructured data and XML
The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
Introduction to algorithms
Communication and Concurrency
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
ACM Transactions on Computational Logic (TOCL)
ICDT '97 Proceedings of the 6th International Conference on Database Theory
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
On the Approximability of the Maximum Common Subgraph Problem
STACS '92 Proceedings of the 9th Annual Symposium on Theoretical Aspects of Computer Science
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
Computing simulations on finite and infinite graphs
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
The Subgraph Bisimulation Problem
IEEE Transactions on Knowledge and Data Engineering
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
GPLAG: detection of software plagiarism by program dependence graph analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed query evaluation with performance guarantees
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Fast best-effort pattern matching in large attributed graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Information preserving XML schema embedding
ACM Transactions on Database Systems (TODS)
Minimization of tree pattern queries with constraints
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Distributed XML processing: Theory and applications
Journal of Parallel and Distributed Computing
TALE: A Tool for Approximate Large Graph Matching
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Querying Communities in Relational Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Distance-join: pattern match query in a large graph database
Proceedings of the VLDB Endowment
Managing and Mining Graph Data
Managing and Mining Graph Data
From polynomial time queries to graph structure theory
Proceedings of the 13th International Conference on Database Theory
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Detecting Social Positions Using Simulation
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Graph pattern matching: from intractable to polynomial time
Proceedings of the VLDB Endowment
Graph homomorphism revisited for graph matching
Proceedings of the VLDB Endowment
Adding regular expressions to graph reachability and pattern queries
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Keyword search in graphs: finding r-cliques
Proceedings of the VLDB Endowment
Capturing topology in graph pattern matching
Proceedings of the VLDB Endowment
Distributed graph pattern matching
Proceedings of the 21st international conference on World Wide Web
Query preserving graph compression
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Towards effective partition management for large graphs
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Performance guarantees for distributed reachability queries
Proceedings of the VLDB Endowment
NeMa: fast graph search with label similarity
Proceedings of the VLDB Endowment
Incremental graph pattern matching
ACM Transactions on Database Systems (TODS)
Diversified top-k graph pattern matching
Proceedings of the VLDB Endowment
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Graph pattern matching is finding all matches in a data graph for a given pattern graph and is often defined in terms of subgraph isomorphism, an NP-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead. These extensions allow graph pattern matching to be conducted in cubic time. However, they fall short of capturing the topology of data graphs, that is, graphs may have a structure drastically different from pattern graphs they match, and the matches found are often too large to understand and analyze. To rectify these problems, this article proposes a notion of strong simulation, a revision of graph simulation for graph pattern matching. (1) We identify a set of criteria for preserving the topology of graphs matched. We show that strong simulation preserves the topology of data graphs and finds a bounded number of matches. (2) We show that strong simulation retains the same complexity as earlier extensions of graph simulation by providing a cubic-time algorithm for computing strong simulation. (3) We present the locality property of strong simulation which allows us to develop an effective distributed algorithm to conduct graph pattern matching on distributed graphs. (4) We experimentally verify the effectiveness and efficiency of these algorithms using both real-life and synthetic data.