Recurrent Path Index for Efficient Graph Traversal

Kusu, Kazuma; Hatano, Kenji

doi:10.1109/bigdata47090.2019.9006295

Cited by 1 publication

(1 citation statement)

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“…The research community has proposed indexing schemes for paths (in particular, shortest paths) in non-temporal (static) graphs. For example, Kusu and Hatano [39] index the shortest path between every two nodes in a subgraph of a given one. Hassan et al [30] propose a method to index paths usually mentioned in queries (these paths are called recurrent).…”

Section: Temporal Indicesmentioning

confidence: 99%

A model and query language for temporal graph databases

et al. 2021

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Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real-world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database.This paper introduces a temporal graph data model, where nodes and relationships contain attributes (properties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like "Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this happened". As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side

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Section: Temporal Indicesmentioning

confidence: 99%