TY - GEN
T1 - DiCeS
T2 - 12th IEEE International Conference on Cloud Computing, CLOUD 2019
AU - Liakos, Panagiotis
AU - Papakonstantinopoulou, Katia
AU - Ntoulas, Alexandros
AU - Delis, Alex
N1 - Funding Information:
under grant agreement № 641515. Alexandros Ntoulas contributed to this work prior joining LinkedIn.
Funding Information:
Partial support for this work has been provided by the European Union Horizon 2020 Programme, project “Galena”,
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - We consider the problem of uncovering communities in complex real-world networks whose nodes and their respective associations originate in streams of data. Although community detection has received much attention in centralized settings, the prevalence of online social networks has resulted in unprecedented volumes of data whose handling calls for novel streaming approaches. Moreover, bursty production of network interactions necessitates cloud-enabled techniques that can both deal with diverse data rates and deploy more computing resources on the fly for improved performance yields. We propose a distributed streaming community detection approach termed DiCeS, and implement it as a cloud application. While seeking communities, the novelty of our approach is at balancing the incoming load to a cluster of computing nodes and adjusting the cluster processing capacity in an elastic manner. We also provide fault tolerance by ensuring that temporarily suspended or failed nodes are restored and all edges of the network stream ultimately received their due processing. Lastly, DiCeS is interactive regarding i) updating the target communities, and ii) obtaining results on demand. Our experimental results demonstrate that DiCeS does handle the edges of real-world network streams at impressive rates, allows for near-linear scaling, and outperforms previous non-distributed approaches. While using ground-truth communities for a wide range of large real-word networks, we also show that DiCeS attains improved accuracy if compared to earlier centralized algorithms.
AB - We consider the problem of uncovering communities in complex real-world networks whose nodes and their respective associations originate in streams of data. Although community detection has received much attention in centralized settings, the prevalence of online social networks has resulted in unprecedented volumes of data whose handling calls for novel streaming approaches. Moreover, bursty production of network interactions necessitates cloud-enabled techniques that can both deal with diverse data rates and deploy more computing resources on the fly for improved performance yields. We propose a distributed streaming community detection approach termed DiCeS, and implement it as a cloud application. While seeking communities, the novelty of our approach is at balancing the incoming load to a cluster of computing nodes and adjusting the cluster processing capacity in an elastic manner. We also provide fault tolerance by ensuring that temporarily suspended or failed nodes are restored and all edges of the network stream ultimately received their due processing. Lastly, DiCeS is interactive regarding i) updating the target communities, and ii) obtaining results on demand. Our experimental results demonstrate that DiCeS does handle the edges of real-world network streams at impressive rates, allows for near-linear scaling, and outperforms previous non-distributed approaches. While using ground-truth communities for a wide range of large real-word networks, we also show that DiCeS attains improved accuracy if compared to earlier centralized algorithms.
KW - Cloud
KW - Community detection
KW - Graph streams
UR - http://www.scopus.com/inward/record.url?scp=85072336788&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072336788&partnerID=8YFLogxK
U2 - 10.1109/CLOUD.2019.00058
DO - 10.1109/CLOUD.2019.00058
M3 - Conference contribution
AN - SCOPUS:85072336788
T3 - IEEE International Conference on Cloud Computing, CLOUD
SP - 301
EP - 310
BT - Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services
A2 - Bertino, Elisa
A2 - Chang, Carl K.
A2 - Chen, Peter
A2 - Damiani, Ernesto
A2 - Goul, Michael
A2 - Oyama, Katsunori
PB - IEEE Computer Society
Y2 - 8 July 2019 through 13 July 2019
ER -