@inproceedings{6e14c3383ca5406b8ddeeeee8f153ed1,
title = "STREAMER: A distributed framework for incremental closeness centrality computation",
abstract = "Networks are commonly used to model the traffic patterns, social interactions, or web pages. The nodes in a network do not possess the same characteristics: some nodes are naturally more connected and some nodes can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given node in the network. When the network is dynamic and keeps changing, the relative importance of the nodes also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose Streamer, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined and replicated parallelism and takes NUMA effects into account. It speeds up the maintenance of the CC of a real graph with 916K vertices and 4.3M edges by a factor of 497 using a 64 nodes cluster.",
author = "Sariyuce, \{Ahmet Erdem\} and Erik Saule and Kamer Kaya and Catalyurek, \{Umit V.\}",
year = "2013",
doi = "10.1109/cluster.2013.6702680 ",
language = "English",
isbn = "9781479908981",
series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2013 IEEE International Conference on Cluster Computing, CLUSTER 2013",
address = "United States",
note = "15th IEEE International Conference on Cluster Computing, CLUSTER 2013 ; Conference date: 23-09-2013 Through 27-09-2013",
}