Fast approximation of centrality
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Ranking of Closeness Centrality for Large-Scale Social Networks
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Centralities: capturing the fuzzy notion of importance in social graphs
Proceedings of the Second ACM EuroSys Workshop on Social Network Systems
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Fast centrality approximation in modular networks
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Proceedings of the 10th annual joint conference on Digital libraries
An effective GPU implementation of breadth-first search
Proceedings of the 47th Design Automation Conference
The Structure of the Computer Science Knowledge Network
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Fast Exact Computation of betweenness Centrality in Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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The betweenness centrality metric has always been intriguing for graph analyses and used in various applications. Yet, it is one of the most computationally expensive kernels in graph mining. In this work, we investigate a set of techniques to make the betweenness centrality computations faster on GPUs as well as on heterogeneous CPU/GPU architectures. Our techniques are based on virtualization of the vertices with high degree, strided access to adjacency lists, removal of the vertices with degree 1, and graph ordering. By combining these techniques within a fine-grain parallelism, we reduced the computation time on GPUs significantly for a set of social networks. On CPUs, which can usually have access to a large amount of memory, we used a coarse-grain parallelism. We showed that heterogeneous computing, i.e., using both architectures at the same time, is a promising solution for betweenness centrality. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for networks. In particular, it reduces the computation time of a 234 million edges graph from more than 4 months to less than 12 days.