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TFLabel: a TopologicalFolding Labeling Scheme for Reachability Querying in a Large Graph
, 2013
"... Reachability querying is a basic graph operation with numerous important applications in databases, network analysis, computational biology, software engineering, etc. Although many indexes have been proposed to answer reachability queries, most of them are only efficient for handling relatively sma ..."
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Reachability querying is a basic graph operation with numerous important applications in databases, network analysis, computational biology, software engineering, etc. Although many indexes have been proposed to answer reachability queries, most of them are only efficient for handling relatively small graphs. We propose TFlabel, an efficient and scalable labeling scheme for processing reachability queries. TFlabel is constructed based on a novel topological folding (TF) that recursively folds an input graph into half so as to reduce the label size, thus improving query efficiency. We show that TFlabel is efficient to construct and propose efficient algorithms and optimization schemes. Our experiments verify that TFlabel is significantly more scalable and efficient than the stateoftheart methods in both index construction and query processing.
Hop Doubling Label Indexing for PointtoPoint Distance Querying on ScaleFree Networks
"... We study the problem of pointtopoint distance querying for massive scalefree graphs, which is important for numerous applications. Given a directed or undirected graph, we propose to build an index for answering such queries based on a novel hopdoubling labeling technique. We derive bounds on th ..."
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We study the problem of pointtopoint distance querying for massive scalefree graphs, which is important for numerous applications. Given a directed or undirected graph, we propose to build an index for answering such queries based on a novel hopdoubling labeling technique. We derive bounds on the index size, the computation costs and I/O costs based on the properties of unweighted scalefree graphs. We show that our method is much more efficient and effective compared to the stateoftheart techniques, in terms of both querying time and indexing costs. Our empirical study shows that our method can handle graphs that are orders of magnitude larger than existing methods. 1.
Dynamic and historical shortestpath distance queries on large evolving networks by pruned landmark labeling
 In WWW
, 2014
"... We propose two dynamic indexing schemes for shortestpath and distance queries on large timeevolving graphs, which are useful in a wide range of important applications such as realtime networkaware search and network evolution analysis. To the best of our knowledge, these methods are the first p ..."
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We propose two dynamic indexing schemes for shortestpath and distance queries on large timeevolving graphs, which are useful in a wide range of important applications such as realtime networkaware search and network evolution analysis. To the best of our knowledge, these methods are the first practical exact indexing methods to efficiently process distance queries and dynamic graph updates. We first propose a dynamic indexing scheme for queries on the last snapshot. The scalability and efficiency of its offline indexing algorithm and query algorithm are competitive even with previous static methods. Meanwhile, the method is dynamic, that is, it can incrementally update indices as the graph changes over time. Then, we further design another dynamic indexing scheme that can also answer two kinds of historical queries with regard to not only the latest snapshot but also previous snapshots. Through extensive experiments on real and synthetic evolving networks, we show the scalability and efficiency of our methods. Specifically, they can construct indices from large graphs with millions of vertices, answer queries in microseconds, and update indices in milliseconds.
Simple, Fast, and Scalable Reachability Oracle
"... A reachability oracle (or hop labeling) assigns each vertex v two sets of vertices: Lout(v) and Lin(v), such that u reaches v iff Lout(u) ∩ Lin(v) = ∅. Despite their simplicity and elegance, reachability oracles have failed to achieve efficiency in more than ten years since their introduction: The ..."
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A reachability oracle (or hop labeling) assigns each vertex v two sets of vertices: Lout(v) and Lin(v), such that u reaches v iff Lout(u) ∩ Lin(v) = ∅. Despite their simplicity and elegance, reachability oracles have failed to achieve efficiency in more than ten years since their introduction: The main problem is high construction cost, which stems from a setcover framework and the need to materialize transitive closure. In this paper, we present two simple and efficient labeling algorithms, HierarchicalLabeling and DistributionLabeling, which can work on massive realworld graphs: Their construction time is an order of magnitude faster than the setcover based labeling approach, and transitive closure materialization is not needed. On large graphs, their index sizes and their query performance can now beat the stateoftheart transitive closure compression and online search approaches.
Exact Topk Nearest Keyword Search in Large Networks
"... Topk nearest keyword search has been of interest because of applications ranging from road network location search by keyword to search of information on an RDF repository. We consider the evaluation of a query with a given vertex and a keyword, and the problem is to find a set of k nearest vertic ..."
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Topk nearest keyword search has been of interest because of applications ranging from road network location search by keyword to search of information on an RDF repository. We consider the evaluation of a query with a given vertex and a keyword, and the problem is to find a set of k nearest vertices that contain the keyword. The known algorithms for handling this problem only give approximate answers. In this paper, we propose algorithms for topk nearest keyword search that provide exact solutions and which handle networks of very large sizes. We have also verified the performance of our solutions compared with the bestknown approximation algorithms with experiments on real datasets.