Big graph search: challenges and techniques

Ma, Shuai; Li, Jia; Hu, Chunming; Lin, Xuelian; Huai, Jinpeng

doi:10.1007/s11704-015-4515-1

Cited by 40 publications

(18 citation statements)

References 50 publications

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“…We are to extend our techniques for dynamic graphs, as real-life networks are often dynamic [25], [40]. We are also to explore the possibility of revising routing proxies for directed graphs and other classes of graph queries, e.g., reachability.…”

Section: Resultsmentioning

confidence: 99%

Proxies for Shortest Path and Distance Queries

Feng

et al. 2016

IEEE Trans. Knowl. Data Eng.

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Computing shortest paths and distances is one of the fundamental problems on graphs, and it remains a challenging task today. This article investigates a light-weight data reduction technique for speeding-up shortest path and distance queries on large graphs. To do this, we propose a notion of routing proxies (or simply proxies), each of which represents a small subgraph, referred to as deterministic routing areas (DRAs). We first show that routing proxies hold good properties for speeding-up shortest path and distance queries. Then we design a linear-time algorithm to compute routing proxies and their corresponding DRAs. Finally, we experimentally verify that our solution is a general technique for reducing graph sizes and speeding-up shortest path and distance queries, using real-life large graphs.

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Section: Resultsmentioning

confidence: 99%

Proxies for Shortest Path and Distance Queries

Feng

et al. 2016

IEEE Trans. Knowl. Data Eng.

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“…All the techniques associated with GDBMS and supported in any graph search engine should fulfil so called FAE rule [5]. The FAE rule says that the quality of search engines includes three key factors: Friendliness, Accuracy and Efficiency, i.e., that a good search engine must provide the users with a friendly query interface and highly accurate answers in a fast way.…”

Section: Discussionmentioning

confidence: 99%

Functional querying in graph databases

Pokorný

2017

Vietnam J Comput Sci

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The paper is focused on a functional querying in graph databases. We consider labelled property graph model and mention also the graph model behind XML databases. An attention is devoted to functional modelling of graph databases both at a conceptual and data level. The notions of graph conceptual schema and graph database schema are considered. The notion of a typed attribute is used as a basic structure both on the conceptual and database level. As a formal approach to declarative graph database querying a version of typed lambda calculus is used. This approach allows to use a logic necessary for querying, arithmetic as well as aggregation function. Another advantage is the ability to deal with relations and graphs in one integrated environment.

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“…networks) have been used to organize information in various areas including biology [1], social sciences [2], linguistics [3], and even path planning in robotics (grid networks) [4]. Moreover, the search of information in such networks is more computationally expensive as the correlation between the nodes increase [5]. In addition such networks, even though sparse in a global sense, tend to be dense in a local sense (nearby nodes) [6].…”

Section: Introductionmentioning

confidence: 99%

Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

2020

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Graphs in real-world applications are dynamic both in terms of structures and inputs. Information discovery in such networks, which present dense and deeply connected patterns locally and sparsity globally can be time consuming and computationally costly. In this paper we address the shortest path query in spatio-temporal graphs which is a fundamental graph problem with numerous applications. In spatiotemporal graphs, shortest path query classical algorithms are insufficient or even flawed because information consistency can not be guaranteed between two timestamps and path recalculation is computationally costly. In this work, we address the complexity and dynamicity of the shortest path query in spatio-temporal graphs with a simple, yet effective model based on Reinforcement Learning with Proximal Policy Optimization. Our solution simplifies the problem by decomposing the spatio-temporal graph in two components: a static and a dynamic sub-graph. The static graph, known and immutable, is efficiently solved with A * algorithm. The sub-graphs interconnecting the static graph have unknown dynamics and we address such issue by estimating the unknown dynamic portion of the graph as a Markov Chain which correlates the observations of the agents in the environment and the path to be followed. We then derive an action policy through Proximal Policy Optimization to select the local optimal actions in the Markov Process that will lead to the shortest path, given the estimated system dynamics. We evaluate the system in a simulation environment constructed in Unity3D. In partially structured and unknown environments, with variable environment parameters we've obtained an efficiency 75% greater than the comparable deterministic solution.

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Big graph search: challenges and techniques

Cited by 40 publications

References 50 publications

Proxies for Shortest Path and Distance Queries

Proxies for Shortest Path and Distance Queries

Functional querying in graph databases

Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

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