An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Computers and Intractability; A Guide to the Theory of NP-Completeness
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Introduction to Algorithms
ISTCS '97 Proceedings of the Fifth Israel Symposium on the Theory of Computing Systems (ISTCS '97)
Keyword Searching and Browsing in Databases using BANKS
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Analysis of SIGMOD's co-authorship graph
ACM SIGMOD Record
Graph indexing: a frequent structure-based approach
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A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining Graph Data
Bidirectional expansion for keyword search on graph databases
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Closure-Tree: An Index Structure for Graph Queries
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SAGA: a subgraph matching tool for biological graphs
Bioinformatics
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Fast and practical indexing and querying of very large graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Fg-index: towards verification-free query processing on graph databases
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Graphs-at-a-time: query language and access methods for graph databases
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Taming verification hardness: an efficient algorithm for testing subgraph isomorphism
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GADDI: distance index based subgraph matching in biological networks
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3-HOP: a high-compression indexing scheme for reachability query
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DOGMA: A Disk-Oriented Graph Matching Algorithm for RDF Databases
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Efficient query answering in probabilistic RDF graphs
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Subgraph search over massive disk resident graphs
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
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Large-scale continuous subgraph queries on streams
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TreeSpan: efficiently computing similarity all-matching
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Adaptive optimizations of recursive queries in teradata
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Efficient subgraph similarity all-matching
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Efficient subgraph matching on billion node graphs
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An in-depth comparison of subgraph isomorphism algorithms in graph databases
Proceedings of the VLDB Endowment
Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Graph similarity search with edit distance constraint in large graph databases
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Efficient simrank-based similarity join over large graphs
Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment
SQBC: An efficient subgraph matching method over large and dense graphs
Information Sciences: an International Journal
Hybrid query execution engine for large attributed graphs
Information Systems
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The dramatic proliferation of sophisticated networks has resulted in a growing need for supporting effective querying and mining methods over such large-scale graph-structured data. At the core of many advanced network operations lies a common and critical graph query primitive: how to search graph structures efficiently within a large network? Unfortunately, the graph query is hard due to the NP-complete nature of subgraph isomorphism. It becomes even challenging when the network examined is large and diverse. In this paper, we present a high performance graph indexing mechanism, SPath, to address the graph query problem on large networks. SPath leverages decomposed shortest paths around vertex neighborhood as basic indexing units, which prove to be both effective in graph search space pruning and highly scalable in index construction and deployment. Via SPath, a graph query is processed and optimized beyond the traditional vertex-at-a-time fashion to a more efficient path-at-a-time way: the query is first decomposed to a set of shortest paths, among which a subset of candidates with good selectivity is picked by a query plan optimizer; Candidate paths are further joined together to help recover the query graph to finalize the graph query processing. We evaluate SPath with the state-of-the-art GraphQL on both real and synthetic data sets. Our experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks.