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arXiv · 2306.00606

Scaling Expected Force: Efficient Identification of Key Nodes in Network-based Epidemic Models

Paolo Sylos Labini·Andrej Jurco·Matteo Ceccarello·Stefano Guarino·Enrico Mastrostefano·Flavio Vella
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Abstract

Centrality measures are fundamental tools of network analysis as they highlight the key actors within the network. This study focuses on a newly proposed centrality measure, Expected Force (EF), and its use in identifying spreaders in network-based epidemic models. We found that EF effectively predicts the spreading power of nodes and identifies key nodes and immunization targets. However, its high computational cost presents a challenge for its use in large networks. To overcome this limitation, we propose two parallel scalable algorithms for computing EF scores: the first algorithm is based on the original formulation, while the second one focuses on a cluster-centric approach to improve efficiency and scalability. Our implementations significantly reduce computation time, allowing for the detection of key nodes at large scales. Performance analysis on synthetic and real-world networks demonstrates that the GPU implementation of our algorithm can efficiently scale to networks with up to 44 million edges by exploiting modern parallel architectures, achieving speed-ups of up to 300x, and 50x on average, compared to the simple parallel solution.

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Paolo Sylos Labini, Andrej Jurco, Matteo Ceccarello, Stefano Guarino, Enrico Mastrostefano, Flavio Vella. 2023-06-01. Scaling Expected Force: Efficient Identification of Key Nodes in Network-based Epidemic Models. https://arxiv.org/abs/2306.00606

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