Graph exploration and search

Davide Mottin, Yinghui Wu

Sakr Sherif and Albert Zomay (eds), Encyclopedia of Big Data Technologies. Springer, Cham


The research on graph exploration has revolved around three main pillars: keyword graph queries, exploratory graph analysis, and refinement of query results.


Exploratory methods have been proposed as a mean to extract knowledge from relational data without knowing what to search (Idreos et al. 2015). Graph exploration has been introduced to perform exploratory analyses on graph-shaped data (Mottin and Müller 2017). Graph exploration aims at mitigating the access to the data to the user, even if such user is a novice.

Algorithms for graph exploration assume the user is not able to completely specify the object of interest with a structured query like a SPARQL (see chapter “Graph Query Languages”), but rather expresses the need with a simpler, more ambiguous language.

This asymmetry between the rigidity of structured queries and ambiguity of the user has inspired the study of approximate, flexible, and example-based methods.