Results 1 
5 of
5
R.F.: Robust exact distance queries on massive networks
, 2014
"... We present a versatile and scalable algorithm for computing exact distances on realworld networks with tens of millions of arcs in real time. Unlike existing approaches, preprocessing and queries are practical on a wide variety of inputs, such as social, communication, sensor, and road networks. W ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
We present a versatile and scalable algorithm for computing exact distances on realworld networks with tens of millions of arcs in real time. Unlike existing approaches, preprocessing and queries are practical on a wide variety of inputs, such as social, communication, sensor, and road networks. We achieve this by providing a unified approach based on the concept of 2hop labels, improving upon existing methods. In particular, we introduce a fast samplingbased algorithm to order vertices by importance, as well as effective compression techniques.
Robust distance queries on massive networks
 In Proceedings of the 22nd Annual European Symposium on Algorithms (ESA’14), Lecture Notes in Computer Science
, 2014
"... Abstract. We present a versatile and scalable algorithm for computing exact distances on realworld networks with tens of millions of arcs in real time. Unlike existing approaches, preprocessing and queries are practical on a wide variety of inputs, such as social, communication, sensor, and road n ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract. We present a versatile and scalable algorithm for computing exact distances on realworld networks with tens of millions of arcs in real time. Unlike existing approaches, preprocessing and queries are practical on a wide variety of inputs, such as social, communication, sensor, and road networks. We achieve this by providing a unified approach based on the concept of 2hop labels, improving upon existing methods. In particular, we introduce a fast samplingbased algorithm to order vertices by importance, as well as effective compression techniques.
Optimal Enumeration: Efficient Topk Tree Matching
"... Driven by many real applications, graph pattern matching has attracted a great deal of attention recently. Consider that a twigpattern matching may result in an extremely large number of matches in a graph; this may not only confuse users by providing too many results but also lead to high computa ..."
Abstract
 Add to MetaCart
(Show Context)
Driven by many real applications, graph pattern matching has attracted a great deal of attention recently. Consider that a twigpattern matching may result in an extremely large number of matches in a graph; this may not only confuse users by providing too many results but also lead to high computational costs. In this paper, we study the problem of topk tree pattern matching; that is, given a rooted tree T, compute its topk matches in a directed graph G based on the twigpattern matching semantics. We firstly present a novel and optimal enumeration paradigm based on the principle of Lawler’s procedure. We show that our enumeration algorithm runs in O(nT + log k) time in each round where nT is the number of nodes in T. Considering that the time complexity to output a match of T is O(nT) and nT ≥ log k in practice, our enumeration technique is optimal. Moreover, the cost of generating top1 match of T in our algorithm is O(mR) where mR is the number of edges in the transitive closure of a data graph G involving all relevant nodes to T. O(mR) is also optimal in the worst case without preknowledge of G. Consequently, our algorithm is optimal with the running time O(mR + k(nT + log k)) in contrast to the time complexity O(mR log k+knT (log k+dT)) of the existing technique where dT is the maximal node degree in T. Secondly, a novel priority based access technique is proposed, which greatly reduces the number of edges accessed and results in a significant performance improvement. Finally, we apply our techniques to the general form of topk graph pattern matching problem (i.e., query is a graph) to improve the existing techniques. Comprehensive empirical studies demonstrate that our techniques may improve the existing techniques by orders of magnitude. 1.
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 ..."
Abstract
 Add to MetaCart
(Show Context)
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.
Optimal Enumeration: Efficient Topk Tree Matching
"... Driven by many real applications, graph pattern matching has attracted a great deal of attention recently. Consider that a twigpattern matching may result in an extremely large number of matches in a graph; this may not only confuse users by providing too many results but also lead to high computa ..."
Abstract
 Add to MetaCart
(Show Context)
Driven by many real applications, graph pattern matching has attracted a great deal of attention recently. Consider that a twigpattern matching may result in an extremely large number of matches in a graph; this may not only confuse users by providing too many results but also lead to high computational costs. In this paper, we study the problem of topk tree pattern matching; that is, given a rooted tree T, compute its topk matches in a directed graph G based on the twigpattern matching semantics. We firstly present a novel and optimal enumeration paradigm based on the principle of Lawler’s procedure. We show that our enumeration algorithm runs in O(nT + log k) time in each round where nT is the number of nodes in T. Considering that the time complexity to output a match of T is O(nT) and nT ≥ log k in practice, our enumeration technique is optimal. Moreover, the cost of generating top1 match of T in our algorithm is O(mR) where mR is the number of edges in the transitive closure of a data graph G involving all relevant nodes to T. O(mR) is also optimal in the worst case without preknowledge of G. Consequently, our algorithm is optimal with the running time O(mR + k(nT + log k)) in contrast to the time complexity O(mR log k+knT (log k+dT)) of the existing technique where dT is the maximal node degree in T. Secondly, a novel priority based access technique is proposed, which greatly reduces the number of edges accessed and results in a significant performance improvement. Finally, we apply our techniques to the general form of topk graph pattern matching problem (i.e., query is a graph) to improve the existing techniques. Comprehensive empirical studies demonstrate that our techniques may improve the existing techniques by orders of magnitude. 1.