Graph Analytics




Graphs provide a natural way of representing complex data. In rrecent years, graph anlytics has proliferated rapidly, and it has useful applications across a wide range of fields, such as social science, computer science, biology and archaeology. However, graph analytics is often computationally expensive. Regardless of implementation details that different data management systems may have, the need to capture semantics remains. Can the efficiency of graph analytics be improved by leveraging their semantics such as in the form of knowledge graphs? 




The project aims to investigate: (1) how to effectively represent and manage the semantics of graph-structured data in the form of knowledge graphs, and (2) how to support efficient querying over graph-structured data using knowledge graphs.

Background Literature

(1) Survey of Graph Database Models, R Angles, C Gutierrez, ACM Computing Surveys, 2008. (2) Constructions from dots and lines, MA Rodriguez, P Neubauer Bulletin of the American Society, 2010. (3) Facebook Announces Its Third Pillar "Graph Search" That Gives You Answers, Not Links Like Google. TechCrunch. 15 January 2013. Retrieved 16 January 2013. (4) Graph Databases: The New Way to Access Super Fast Social Data, Emil Eifrem, 2012.

Updated:  1 August 2018/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing