2018
Karyotis, V.; Tsitseklis, K.; Sotiropoulos, K.; Papavassiliou, S.
Enhancing Community Detection for Big Sensor Data Clustering via Hyperbolic Network Embedding Conference
Institute of Electrical and Electronics Engineers Inc., 2018, ISBN: 9781538632277, (cited By 1; Conference of 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 ; Conference Date: 19 March 2018 Through 23 March 2018; Conference Code:140607).
Abstract | Links | BibTeX | Tags: Big data; Cluster analysis; Graph theory; Population dynamics; Sensor networks; Ubiquitous computing, Clustering algorithms, Community detection; Data clustering; Edge betweenness centrality; Hyperbolic networks; Newman algorithms; Rigel embedding
@conference{Karyotis2018266,
title = {Enhancing Community Detection for Big Sensor Data Clustering via Hyperbolic Network Embedding},
author = {V. Karyotis and K. Tsitseklis and K. Sotiropoulos and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056458909&doi=10.1109%2fPERCOMW.2018.8480134&partnerID=40&md5=082f7ba01a225e8ccef49047803f02bd},
doi = {10.1109/PERCOMW.2018.8480134},
isbn = {9781538632277},
year = {2018},
date = {2018-01-01},
journal = {2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018},
pages = {266-271},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this paper we present a novel big data clustering approach for measurements obtained from pervasive sensor networks. To address the potential very large scale of such datasets, we map the problem of data clustering to a community detection one. Datasets are cast in the form of graphs, representing the relations among individual observations and data clustering is mapped to node clustering (community detection) in the data graph. We propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, making it possible to compute more efficiently the hyperbolic edge-betweenness centrality (HEBC) needed in the modified GN algorithm. This allows for more efficient clustering of the nodes of the data graph without significantly sacrificing accuracy. We demonstrate the efficacy of our approach with artificial network and data topologies, and real benchmark datasets. The proposed methodology can be used for efficient clustering of datasets obtained from massive pervasive smart city/building sensor networks, such as the FIESTA-IoT platform, and exploited in various applications such as lower-cost sensing. © 2018 IEEE.},
note = {cited By 1; Conference of 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 ; Conference Date: 19 March 2018 Through 23 March 2018; Conference Code:140607},
keywords = {Big data; Cluster analysis; Graph theory; Population dynamics; Sensor networks; Ubiquitous computing, Clustering algorithms, Community detection; Data clustering; Edge betweenness centrality; Hyperbolic networks; Newman algorithms; Rigel embedding},
pubstate = {published},
tppubtype = {conference}
}
In this paper we present a novel big data clustering approach for measurements obtained from pervasive sensor networks. To address the potential very large scale of such datasets, we map the problem of data clustering to a community detection one. Datasets are cast in the form of graphs, representing the relations among individual observations and data clustering is mapped to node clustering (community detection) in the data graph. We propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, making it possible to compute more efficiently the hyperbolic edge-betweenness centrality (HEBC) needed in the modified GN algorithm. This allows for more efficient clustering of the nodes of the data graph without significantly sacrificing accuracy. We demonstrate the efficacy of our approach with artificial network and data topologies, and real benchmark datasets. The proposed methodology can be used for efficient clustering of datasets obtained from massive pervasive smart city/building sensor networks, such as the FIESTA-IoT platform, and exploited in various applications such as lower-cost sensing. © 2018 IEEE.