USING EVOLUTIONARY ALGORITHM AND GPGPU
FOR FINDING INFLUENTIAL NODES IN SOCIAL NETWORKS (#39)
Radosław Michalski, Michał Weskida
[email protected], [email protected]
Department of Computational Intelligence
Wrocław University of Science and Technology
Wrocław, Poland
Research question: Is it possible to find good influencers fast if we use evolutionary algorithm?
Assumptions: Directed network, the influence model is linear threshold, uniform threshold = 0.5,
weights computed as the frequency of communication between users.
Comparison: greedy algorithm
Datasets: UC Irvine, Digg, Facebook (all come from KONECT: http://konect.uni-koblenz.de)
Evolutionary algoritm parameters
GPGPU approach
We realized that we can use GPGPU for simulating the
process of influence diffusion and for looking for seeds
(in the case of evolutionary and greedy algorithms).
HW/SW: NVIDIA K2200 (640 cores), CUDA 7.5, Linux
Results
Results
General outcome:
Same time – better seeds.
Same # of inf – better time.
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Limitations:
smaller networks (overcomed)
need to learn parameters
on a given network
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Future plans:
fine-tuning of the algorithm
cross-dataset model
parametrization
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Weskida, M., Michalski, R.: Evolutionary Algorithm for Seed Selection in Social Influence Process. SNAA 2016 Workshop at ASONAM 2016,
The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1189-1196
Acknowledgments
www.datasciencegroup.pl
This work was supported by the National Science Centre, the decision no. DEC-2015/17/D/ST6/04046
Details of the project: https://www.ii.pwr.edu.pl/~michalski/research.php?project=infmaxgen