Toward a distance oracle for billion-node graphs

Qi, Zichao; Shao, Bin; Wang, Haixun

doi:10.14778/2732219.2732225

Cited by 36 publications

(17 citation statements)

References 37 publications

(67 reference statements)

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“…Selecting landmarks or reference points to facilitate the shortest path distance computation has been adopted in many works [26,28,29]. Existing landmark selection criteria are quite biased according to different graph structures and applications.…”

Section: Landmark Selectionmentioning

confidence: 99%

“…Various graph indexing techniques have been proposed to speed up the query evaluation, like the embedding technique introduced in GStore [39] for efficient SPARQL query processing, independent set-based labeling [10], the distance oracle approach [28] and etc. However, effective generic index structures for adhoc graph queries are spaceconsuming and involve a long setup time.…”

Section: Introductionmentioning

confidence: 99%

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Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

Zhang

Chen

2017

Data Sci. Eng.

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Performing online selective queries against graphs is a challenging problem due to the unbounded nature of graph queries which leads to poor computation locality. It becomes even difficult when a graph is too large to be fit in the memory. Although there have been emerging efforts on managing large graphs in a distributed and parallel setting, e.g., Pregel, HaLoop and etc, these computing frameworks are designed from the perspective of scalability instead of the query efficiency. In this work, we present our solution methodology for online selective graph queries based on the shortest path distance semantic, which finds various applications in practice. The essential intuition is to build a distance-aware index for online distance-based query processing and to eliminate redundant graph traversal as much as possible. We discuss how the solution can be applied to two types of research problems, distance join and vertex set bonding, which are distancebased graph pattern discovery and finding the structurewise bonding of vertices, respectively.

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Section: Landmark Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

Zhang

Chen

2017

Data Sci. Eng.

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“…• Use approximation or oracle-based techniques to estimate the shortest distance between two vertices in the subgraph (e.g. as in [7,23,24,27]). If the vertices are close in the graph (e.g., within the same community), such techniques will estimate distances between vertices that are very close to the true distances.…”

Section: Scalability Considerationsmentioning

confidence: 99%

To Be Connected, or Not to Be Connected

Ruchansky¹,

Bonchi²,

García-Soriano³

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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We study the problem of extracting a selective connector for a given set of query vertices Q ⊆ V in a graph G = (V , E). A selective connector is a subgraph of G which exhibits some cohesiveness property, and contains the query vertices but does not necessarily connect them all. Relaxing the connectedness requirement allows the connector to detect multiple communities and to be tolerant to outliers. We achieve this by introducing the new measure of network ine ciency and by instantiating our search for a selective connector as the problem of nding the minimum ine ciency subgraph.We show that the minimum ine ciency subgraph problem is NPhard, and devise e cient algorithms to approximate it. By means of several case studies in a variety of application domains (such as human brain, cancer, and food networks), we show that our minimum ine ciency subgraph produces high-quality solutions, exhibiting all the desired behaviors of a selective connector.

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“…To address this, in practice, one pre-computes a data structure called a distance oracle that supports approximate shortest distance queries between two nodes with logarithmic query time. Solutions such as [14,38,40,11,8,10,12] carefully select seed nodes (also known as landmarks) and store the shortest distances from all the nodes to the seeds. The advantage of using such a data structure is that they are compact and the query time is very fast.…”

Section: Related Workmentioning

confidence: 99%

Grecs

Meng

Kamara

Nissim

et al. 2015

Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security

103

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We propose graph encryption schemes that efficiently support approximate shortest distance queries on large-scale encrypted graphs. Shortest distance queries are one of the most fundamental graph operations and have a wide range of applications. Using such graph encryption schemes, a client can outsource large-scale privacy-sensitive graphs to an untrusted server without losing the ability to query it. Other applications include encrypted graph databases and controlled disclosure systems. We propose GRECS (stands for GRaph EnCryption for approximate Shortest distance queries) which includes three oracle encryption schemes that are provably secure against any semi-honest server. Our first construction makes use of only symmetric-key operations, resulting in a computationally-efficient construction. Our second scheme makes use of somewhat-homomorphic encryption and is less computationally-efficient but achieves optimal communication complexity (i.e. uses a minimal amount of bandwidth). Finally, our third scheme is both computationally-efficient and achieves optimal communication complexity at the cost of a small amount of additional leakage. We implemented and evaluated the efficiency of our constructions experimentally. The experiments demonstrate that our schemes are efficient and can be applied to graphs that scale up to 1.6 million nodes and 11 million edges.

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Toward a distance oracle for billion-node graphs

Cited by 36 publications

References 37 publications

Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

To Be Connected, or Not to Be Connected

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