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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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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.
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.
Distance oracles in edgelabeled graphs
"... A fundamental operation over edgelabeled graphs is the computation of shortestpath distances subject to a constraint on the set of permissible edge labels. Applying exact algorithms for such an operation is not a viable option, especially for massive graphs, or in scenarios where the distance com ..."
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A fundamental operation over edgelabeled graphs is the computation of shortestpath distances subject to a constraint on the set of permissible edge labels. Applying exact algorithms for such an operation is not a viable option, especially for massive graphs, or in scenarios where the distance computation is used as a primitive for more complex computations. In this paper we study the problem of efficient approximation of shortestpath queries with edgelabel constraints, for which we devise two indexes based on the idea of landmarks: distances from all vertices of the graph to a selected subset of landmark vertices are precomputed and then used at query time to efficiently approximate distance queries. The major challenge to face is that, in principle, an exponential number of constraint label sets needs to be stored for each vertexlandmark pair, which makes the index precomputation and storage far from trivial. We tackle this challenge from two different perspectives, which lead to indexes with different characteristics: one index is faster and more accurate, but it requires more space than the other. We extensively evaluate our techniques on real and synthetic datasets, showing that our indexes can efficiently and accurately estimate labelconstrained distance queries. 1.
RouteSaver: Leveraging Route APIs for Accurate and Efficient Query Processing at LocationBased Services
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (TKDE)
"... Locationbased services (LBS) enable mobile users to query pointsofinterest (e.g., restaurants, cafes) on various features (e.g., price, quality, variety). In addition, users require accurate query results with uptodate travel times. Lacking the monitoring infrastructure for road traffic, the LB ..."
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Locationbased services (LBS) enable mobile users to query pointsofinterest (e.g., restaurants, cafes) on various features (e.g., price, quality, variety). In addition, users require accurate query results with uptodate travel times. Lacking the monitoring infrastructure for road traffic, the LBS may obtain live travel times of routes from online route APIs in order to offer accurate results. Our goal is to reduce the number of requests issued by the LBS significantly while preserving accurate query results. First, we propose to exploit recent routes requested from route APIs to answer queries accurately. Then, we design effective lower/upper bounding techniques and ordering techniques to process queries efficiently. Also, we study parallel route requests to further reduce the query response time. Our experimental evaluation shows that our solution is 3 times more efficient than a competitor, and yet achieves high result accuracy (above 98%).
Received in revised form
, 2014
"... searching nearby restaurants [7,5]. Various types of spatial rocessing spatial types of locationn road navigation test path searching e submitted from ortest paths from current locations to their specific destinations. We consider of a big city, e.g., Hong Kong. The roads in these cities contain a ..."
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searching nearby restaurants [7,5]. Various types of spatial rocessing spatial types of locationn road navigation test path searching e submitted from ortest paths from current locations to their specific destinations. We consider of a big city, e.g., Hong Kong. The roads in these cities contain a lot of traffic lights, junctions, small single lane Contents lists available at ScienceDirect journal homepage: www.else Information n Corresponding author.
MemoryEfficient Fast Shortest Path Estimation in Large Social Networks
"... As the sizes of contemporary social networks surpass billions of users, so grows the need for fast graph algorithms to analyze them. A particularly important basic operation is the computation of shortest paths between nodes. Classical exact algorithms for this problem are prohibitively slow on ..."
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As the sizes of contemporary social networks surpass billions of users, so grows the need for fast graph algorithms to analyze them. A particularly important basic operation is the computation of shortest paths between nodes. Classical exact algorithms for this problem are prohibitively slow on large graphs, which motivates the development of approximate methods. Of those, landmarkbased methods have been actively studied in recent years. Landmarkbased estimation methods start by picking a fixed set of landmark nodes, precomputing the distance from each node in the graph to each landmark, and storing the precomputed distances in a data structure. Prior work has shown that the number of landmarks required to achieve a given level of precision grows with the size of the graph. Simultaneously, the size of the data structure is proportional to the product of the size of the graph and the number of landmarks. In this work we propose an alternative landmarkbased distance estimation approach that substantially reduces space requirements by means of pruning: computing distances from each node to only a small subset of the closest landmarks. We evaluate our method on the DBLP, Orkut, Twitter and Skype social networks and demonstrate that the resulting estimation algorithms are comparable in query time and potentially superior in approximation quality to equivalent nonpruned landmarkbased methods, while requiring less memory or disk space. 1
Finding the CostOptimal Path with Time Constraint over TimeDependent Graphs
"... Shortest path query is an important problem and has been well studied in static graphs. However, in practice, the costs of edges in graphs always change over time. We call such graphs as timedependent graphs. In this paper, we study how to find a costoptimal path with time constraint in timedepend ..."
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Shortest path query is an important problem and has been well studied in static graphs. However, in practice, the costs of edges in graphs always change over time. We call such graphs as timedependent graphs. In this paper, we study how to find a costoptimal path with time constraint in timedependent graphs. Most existing works regarding the TimeDependent Shortest Path (TDSP) problem focus on finding a shortest path with the minimum travel time. All these works are based on the following fact: the earliest arrival time at a vertex v can be derived from the earliest arrival time at v’s neighbors. Unfortunately, this fact does not hold for our problem. In this paper, we propose a novel algorithm to compute a costoptimal path with time constraint in timedependent graphs. We show that the time and space complexities of our algorithm are O(knlogn + mk) and O((n + m)k) respectively. We confirm the effectiveness and efficiency of our algorithm through conducting experiments on real datasets with synthetic cost. 1.