SuMGra: Querying Multigraphs via Efficient Indexing

Vijay Ingalalli (1, 2) , Dino Ienco (1) , Pascal Poncelet (2)

1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 ADVANSE - ADVanced Analytics for data SciencE

Many real world datasets can be represented by a network with a set of nodes interconnected with each other by multiple relations. Such a rich graph is called a multigraph. Unfortunately, all the existing algorithms for subgraph query matching are not able to adequately leverage multiple relationships that exist between the nodes. In this paper we propose an efficient indexing schema for querying single large multi-graphs, where the indexing schema aptly captures the neighbourhood structure in the data graph. Our proposal SuMGra couples this novel indexing schema with a subgraph search algorithm to quickly traverse though the solution space to enumerate all the matchings. Extensive experiments conducted on real benchmarks prove the time efficiency as well as the scalability of SuMGra.

Mots clés: Query Processing; Structural Connectivity; Indexing Schema; Subgraph Isomorphism; Edge Dimension

Domaines: Base de données [cs.DB]

Vijay Ingalalli, Dino Ienco, Pascal Poncelet. SuMGra: Querying Multigraphs via Efficient Indexing. DEXA 2016 - 27th International Conference on Database and Expert Systems Applications, Sep 2016, Porto, Portugal. pp.387-401, ⟨10.1007/978-3-319-44403-1_24⟩. ⟨lirmm-01362431⟩