A survey of computation techniques on time evolving graphs. (19th March 2020)

• Record Type: Journal Article
• Authors: Sharma, Shalini; Chou, Jerry
• Is Part Of: International journal of big data intelligence. Volume 7:Number 1(2020)
• Page Start: 1
• Page End: 14
• Publication Date: 2020-03-19
• Subjects: big data -- time evolving graphs -- TEGs -- computing framework -- algorithm -- data mining; Big data -- Periodicals; 005.705
• Languages: English
• ISSNs: 2053-1389

Abstract

Analysing time evolving graphs (TEG) in a timely fashion is critical in many application domains, such as social network. Many research efforts have been made with to address the challenges of volume and velocity from dealing with such datasets. However, it remains to be an active and challenged research topic. Therefore, in this survey, we summarise the state-of-art computation techniques for TEG. We collect these techniques from three different research communities: 1) the data mining for graph analysis; 2) the theory for graph algorithm; 3) the computation for graph computing framework. Based on our study, we also propose our own computing framework DASH for TEG. We have even performed some experiments by comparing DASH and graph processing system (GPS). We are optimistic that this paper will help many researchers to understand various dimensions of problems in TEG and continue developing the necessary techniques to resolve these problems more efficiently.