Reinforcement Learning Based Monte Carlo Tree Search for Temporal Path Discovery

Ding, Pengfei; Liu, Guanfeng; Liu, An; Li, Zhixu; Zheng, Kai

doi:10.1109/icdm.2019.00024

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Finding optimal paths in the presence of constraints is the focus of another group of methods. Examples are the works of [10,11] for finding approximate optimal paths. In the problems studied by Hassan et al [12], edge labels are used for classification purposes.…”

Section: Related Workmentioning

confidence: 99%

Path Algorithms for Contact Sequence Temporal Graphs

Gheibi,

Banerjee,

Ranka

et al. 2024

Algorithms

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This paper proposes a new time-respecting graph (TRG) representation for contact sequence temporal graphs. Our representation is more memory-efficient than previously proposed representations and has run-time advantages over the ordered sequence of edges (OSE) representation, which is faster than other known representations. While our proposed representation clearly outperforms the OSE representation for shallow neighborhood search problems, it is not evident that it does so for different problems. We demonstrate the competitiveness of our TRG representation for the single-source all-destinations fastest, min-hop, shortest, and foremost paths problems.

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Section: Related Workmentioning

confidence: 99%

Path Algorithms for Contact Sequence Temporal Graphs

Gheibi,

Banerjee,

Ranka

et al. 2024

Algorithms

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Path Querying in Graph Databases: A Systematic Mapping Study

García,

Angles

2024

IEEE Access

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Path querying refers to the evaluation of path queries in a graph database. New research in this topic is crucial for the development of graph database systems as path queries are associated with relevant use-cases and application domains. The aim of this article is to identify and establish what is currently known about path querying in graph databases. To achieve this, we conducted a systematic mapping study (SMS) in which we explored four digital libraries and collected research papers published from 1970 to 2022. These articles were filtered, classified and analyzed to extract quantitative and qualitative information which is presented in this article. Additionally, we provide a concise description of keywords, use-cases and application domains associated with path querying in graph databases.

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