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54
Fast and accurate estimation of shortest paths in large graphs
 In Proceedings of Conference on Information and Knowledge Management (CIKM
, 2010
"... Computing shortest paths between two given nodes is a fundamental operation over graphs, but known to be nontrivial over large diskresident instances of graph data. While a numberoftechniquesexistfor answeringreachabilityqueries and approximating node distances efficiently, determining actual short ..."
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Cited by 28 (1 self)
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Computing shortest paths between two given nodes is a fundamental operation over graphs, but known to be nontrivial over large diskresident instances of graph data. While a numberoftechniquesexistfor answeringreachabilityqueries and approximating node distances efficiently, determining actual shortest paths (i.e. the sequence of nodes involved) is often neglected. However, in applications arising in massive online social networks, biological networks, and knowledge graphs it is often essential to find out many, if not all, shortest paths between two given nodes. In this paper, we address this problem and present a scalable sketchbased index structure that not only supports estimation of node distances, but also computes corresponding shortest paths themselves. Generating the actual path information allows for further improvements to the estimation accuracy of distances (and paths), leading to nearexact shortestpath approximations in real world graphs. We evaluate our techniques – implemented within a fully functional RDF graph database system – over large realworld social and biological networks of sizes ranging from tens of thousand to millions of nodes and edges. Experiments on several datasets show that we can achieve query response times providing several orders of magnitude speedup over traditional path computations while keeping the estimation errors between 0 % and 1 % on average.
Y.: Fast exact shortestpath distance queries on large networks by pruned landmark labeling
 In: SIGMOD 2013
, 2013
"... We propose a new exact method for shortestpath distance queries on largescale networks. Our method precomputes distance labels for vertices by performing a breadthfirst search from every vertex. Seemingly too obvious and too inefficient at first glance, the key ingredient introduced here is pruni ..."
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Cited by 22 (1 self)
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We propose a new exact method for shortestpath distance queries on largescale networks. Our method precomputes distance labels for vertices by performing a breadthfirst search from every vertex. Seemingly too obvious and too inefficient at first glance, the key ingredient introduced here is pruning during breadthfirst searches. While we can still answer the correct distance for any pair of vertices from the labels, it surprisingly reduces the search space and sizes of labels. Moreover, we show that we can perform 32 or 64 breadthfirst searches simultaneously exploiting bitwise operations. We experimentally demonstrate that the combination of these two techniques is efficient and robust on various kinds of largescale realworld networks. In particular, our method can handle social networks and web graphs with hundreds of millions of edges, which are two orders of magnitude larger than the limits of previous exact methods, with comparable query time to those of previous methods.
Benefits of bias: Towards better characterization of network sampling
 In SIGKDD
, 2011
"... From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on how best to sample from networks. We investigate connections between specific biases and various measures of structural r ..."
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Cited by 19 (0 self)
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From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on how best to sample from networks. We investigate connections between specific biases and various measures of structural representativeness. We show that certain biases are, in fact, beneficial for many applications, as they “push” the sampling process towards inclusion of desired properties. Finally, we describe how these sampling biases can be exploited in several, realworld applications including disease outbreak detection and market research.
A Continuous Query System for Dynamic Route Planning
"... Abstract—In this paper, we address the problem of answering continuous route planning queries over a road network, in the presence of updates to the delay (cost) estimates of links. A simple approach to this problem would be to recompute the best path for all queries on arrival of every delay update ..."
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Abstract—In this paper, we address the problem of answering continuous route planning queries over a road network, in the presence of updates to the delay (cost) estimates of links. A simple approach to this problem would be to recompute the best path for all queries on arrival of every delay update. However, such a naive approach scales poorly when there are many users who have requested routes in the system. Instead, we propose two new classes of approximate techniques – Kpaths and proximity measures to substantially speed up processing of the set of designated routes specified by continuous route planning queries in the face of incoming traffic delay updates. Our techniques work through a combination of precomputation of likely good paths and by avoiding complete recalculations on every delay update, instead only sending the user new routes when delays change significantly. Based on an experimental evaluation with 7,000 drives from real taxi cabs, we found that the routes delivered by our techniques are within 5 % of the best shortest path and have run times an order of magnitude or less compared to a naive approach. I.
Fast fully dynamic landmarkbased estimation of shortest path distances in very large graphs
 In ACM Conference on Information and Knowledge Management (CIKM
, 2011
"... Computing the shortest path between a pair of vertices in a graph is a fundamental primitive in graph algorithmics. Classical exact methods for this problem do not scale up to contemporary, rapidly evolving social networks with hundreds of millions of users and billions of connections. A number of a ..."
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Cited by 13 (1 self)
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Computing the shortest path between a pair of vertices in a graph is a fundamental primitive in graph algorithmics. Classical exact methods for this problem do not scale up to contemporary, rapidly evolving social networks with hundreds of millions of users and billions of connections. A number of approximate methods have been proposed, including several landmarkbased methods that have been shown to scale up to very large graphs with acceptable accuracy. This paper presents two improvements to existing landmarkbased shortest path estimation methods. The first improvement relates to the use of shortestpath trees (SPTs). Together with appropriate shortcutting heuristics, the use of SPTs allows to achieve higher accuracy with acceptable time and memory overhead. Furthermore, SPTs can be maintained incrementally under edge insertions and deletions, which allows for a fullydynamic algorithm. The second improvement is a new landmark selection strategy that seeks to maximize the coverage of all shortest paths by the selected landmarks. The improved method is evaluated on the DBLP, Orkut, Twitter and Skype social networks.
On kskip Shortest Paths
"... Given two vertices s, t in a graph, let P be the shortest path (SP) from s to t, and P ⋆ a subset of the vertices in P. P ⋆ is a kskip shortest path from s to t, if it includes at least a vertex out of every k consecutive vertices in P. In general, P ⋆ succinctly describes P by sampling the vertice ..."
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Cited by 11 (0 self)
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Given two vertices s, t in a graph, let P be the shortest path (SP) from s to t, and P ⋆ a subset of the vertices in P. P ⋆ is a kskip shortest path from s to t, if it includes at least a vertex out of every k consecutive vertices in P. In general, P ⋆ succinctly describes P by sampling the vertices in P with a rate of at least 1/k. This makes P ⋆ a natural substitute in scenarios where reporting every single vertex of P is unnecessary or even undesired. This paper studies kskip SP computation in the context of spatial network databases (SNDB). Our technique has two properties crucial for realtime query processing in SNDB. First, our solution is able to answer kskip queries significantly faster than finding the original SPs in their entirety. Second, the previous objective is achieved with a structure that occupies less space than storing the underlying road network. The proposed algorithms are the outcome of a careful theoretical analysis that reveals valuable insight into the characteristics of the kskip SP problem. Their efficiency has been confirmed by extensive experiments with real data.
Relational approach for shortest path discovery over large graphs
 Proc. VLDB Endow
"... With the rapid growth of large graphs, we cannot assume that graphs can still be fully loaded into memory, thus the diskbased graph operation is inevitable. In this paper, we take the shortest path discovery as an example to investigate the technique issues when leveraging existing infrastructure o ..."
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Cited by 9 (1 self)
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With the rapid growth of large graphs, we cannot assume that graphs can still be fully loaded into memory, thus the diskbased graph operation is inevitable. In this paper, we take the shortest path discovery as an example to investigate the technique issues when leveraging existing infrastructure of relational database (RDB) in the graph data management. Based on the observation that a variety of graph search queries can be implemented by iterative operations including selecting frontier nodes from visited nodes, making expansion from the selected frontier nodes, and merging the expanded nodes into the visited ones, we introduce a relational FEM framework with three corresponding operators to implement graph search tasks in the RDB context. We show new features such as window function and merge statement introduced by recent SQL standards can not only simplify the expression but also improve the performance of the FEM framework. In addition, we propose two optimization strategies specific to shortest path discovery inside the FEM framework. First, we take a bidirectional set Dijkstra’s algorithm in the path finding. The bidirectional strategy can reduce the search space, and set Dijkstra’s algorithm finds the shortest path in a setatatime fashion. Second, we introduce an index named SegTable to preserve the local shortest segments, and exploit SegTable to further improve the performance. The final extensive experimental results illustrate our relational approach with the optimization strategies achieves high scalability and performance. 1.
Neighborhoodprivacy protected shortest distance computing in cloud
 In SIGMOD Conference
, 2011
"... With the advent of cloud computing, it becomes desirable to utilize cloud computing to efficiently process complex operations in large graphs without compromising their sensitive information. This paper studies shortest distance computing in the cloud, which aims at the following goals: i) preventin ..."
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Cited by 9 (0 self)
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With the advent of cloud computing, it becomes desirable to utilize cloud computing to efficiently process complex operations in large graphs without compromising their sensitive information. This paper studies shortest distance computing in the cloud, which aims at the following goals: i) preventing outsourced graphs from neighborhood attack, ii) preserving shortest distances in outsourced graphs, iii) minimizing overhead on the client side. The basic idea of this paper is to transform an original graph G into a link graph Gl kept locally and a set of outsourced graphs Go. Each outsourced graph should meet the requirement of a new security model called 1neighborhooddradius. In addition, the shortest distance query can be equivalently answered using Gl and Go. Our objective is to minimize the space cost on the client side when both security and utility requirements are satisfied. We devise a greedy method to produce Gl and Go, which can exactly answer the shortest distance queries. We also develop an efficient transformation method to support approximate shortest distance answering under a given additive error bound. The final experimental results illustrate the effectiveness and efficiency of our method.
Online Computation of Fastest Path in TimeDependent Spatial Networks
, 2011
"... The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. Howe ..."
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Cited by 8 (5 self)
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The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. However, with realworld spatial networks the edge traveltimes are timedependent, where the arrivaltime to an edge determines the actual traveltime on the edge. In this paper, we study the online computation of fastest path in timedependent spatial networks and present a technique which speedsup the path computation. We show that our fastest path computation based on a bidirectional timedependent A * search significantly improves the computation time and storage complexity. With extensive experiments using real datasets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.