Large graph clustering using DCT-based graph clustering

Nikolaos Tsapanos, Anastasios Tefas, Nikolaos Nikolaidis, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
340 Downloads (Pure)


With the proliferation of the World Wide Web, graph structures have arisen on social network/media sites. Such graphs usually number several million nodes, i.e., they can be characterized as Big Data. Graph clustering is an important analysis tool for other graph related tasks, such as compression, community discovery and recommendation systems, to name a few. We propose a novel extension to a graph clustering algorithm, that attempts to cluster a graph, through the optimization of
selected terms of the graph weight/adjacency matrix Discrete Cosine Transform.
Original languageEnglish
Title of host publication2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD 2014)
Subtitle of host publicationProceedings of a meeting held 9-12 December 2014, Orlando, Florida, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781479953752
ISBN (Print)9781479945399
Publication statusPublished - Mar 2015
EventIEEE Symposium on Computational Intelligence in Big Data (CIBD) - Orlando, United States
Duration: 9 Dec 201412 Dec 2014


ConferenceIEEE Symposium on Computational Intelligence in Big Data (CIBD)
Country/TerritoryUnited States


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