Large graph clustering using DCT-based graph clustering

Nikolaos Tsapanos; Anastasios Tefas; Nikolaos Nikolaidis; Ioannis Pitas

doi:10.1109/CIBD.2014.7011536

Large graph clustering using DCT-based graph clustering

Nikolaos Tsapanos, Anastasios Tefas, Nikolaos Nikolaidis, Ioannis Pitas

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

2 Citations (Scopus)

390 Downloads (Pure)

Abstract

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 language	English
Title of host publication	2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD 2014)
Subtitle of host publication	Proceedings of a meeting held 9-12 December 2014, Orlando, Florida, USA
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	4
ISBN (Electronic)	9781479953752
ISBN (Print)	9781479945399
DOIs	https://doi.org/10.1109/CIBD.2014.7011536
Publication status	Published - Mar 2015
Event	IEEE Symposium on Computational Intelligence in Big Data (CIBD) - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014

Conference

Conference	IEEE Symposium on Computational Intelligence in Big Data (CIBD)
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14

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10.1109/CIBD.2014.7011536

Ioannis Pitas - Large Graph Clustering Using DCT-Based Graph Clustering - 2014
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7011536. Please refer to any applicable terms of use of the publisher.
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title = "Large graph clustering using DCT-based graph clustering",

abstract = "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 ofselected terms of the graph weight/adjacency matrix Discrete Cosine Transform.",

author = "Nikolaos Tsapanos and Anastasios Tefas and Nikolaos Nikolaidis and Ioannis Pitas",

year = "2015",

month = mar,

doi = "10.1109/CIBD.2014.7011536",

language = "English",

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booktitle = "2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD 2014)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "IEEE Symposium on Computational Intelligence in Big Data (CIBD) ; Conference date: 09-12-2014 Through 12-12-2014",

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Tsapanos, N, Tefas, A, Nikolaidis, N & Pitas, I 2015, Large graph clustering using DCT-based graph clustering. in 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD 2014): Proceedings of a meeting held 9-12 December 2014, Orlando, Florida, USA. Institute of Electrical and Electronics Engineers (IEEE), IEEE Symposium on Computational Intelligence in Big Data (CIBD), Orlando, United States, 9/12/14. https://doi.org/10.1109/CIBD.2014.7011536

Large graph clustering using DCT-based graph clustering. / Tsapanos, Nikolaos; Tefas, Anastasios; Nikolaidis, Nikolaos et al.
2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD 2014): Proceedings of a meeting held 9-12 December 2014, Orlando, Florida, USA. Institute of Electrical and Electronics Engineers (IEEE), 2015.

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

TY - GEN

T1 - Large graph clustering using DCT-based graph clustering

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AU - Tefas, Anastasios

AU - Nikolaidis, Nikolaos

AU - Pitas, Ioannis

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Y1 - 2015/3

N2 - 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 ofselected terms of the graph weight/adjacency matrix Discrete Cosine Transform.

AB - 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 ofselected terms of the graph weight/adjacency matrix Discrete Cosine Transform.

U2 - 10.1109/CIBD.2014.7011536

DO - 10.1109/CIBD.2014.7011536

M3 - Conference Contribution (Conference Proceeding)

SN - 9781479945399

BT - 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD 2014)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - IEEE Symposium on Computational Intelligence in Big Data (CIBD)

Y2 - 9 December 2014 through 12 December 2014

ER -

Large graph clustering using DCT-based graph clustering

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