Name
Abstract | ||
|---|---|---|
The eigenvalue equation intervenes in models of infectious disease propagation and could be used as an ally of vaccination campaigns in the actions carried out by health care organizations. The stochastic model based on Page Rank allows to simulate the epidemic spread, where a Like-like infection vector is calculated to help establish efficient vaccination strategy. In the context of epidemic spread, generally the damping factor is high. This is because the probability that an infected individual contaminates any other individual through some unusual contact is low. One consequence of this results is that the second largest eigenvalue of Page Rank matrix could be very close to its dominant eigenvalue. Another difficulty arises from the growing size of real networks. Handling very big graph becomes a challenge for computing Page Rank. Furthermore, the high damping factor makes many existing algorithms less efficient. In this paper, we explore the computation methods of Page Rank to address these issues. Specifically, we study the implicitly restarted Arnoldi method (IRAM) and discuss some possible improvements over it. We also present a parallel implementation for IRAM, targeting big data and sparse matrices representing scale-free networks (also known as power law networks). The algorithm is tested on a nation wide cluster of clusters Grid5000. Experiments on very large networks such as twitter, yahoo (over 1 billion nodes) are conducted. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SBAC-PAD.2013.2 | SBAC-PAD |
Keywords | Field | DocType |
health care,infected individual contaminates,stochastic processes,epidemic spread simulation,diseases,grid5000 clusters,big data,epidemic spread,grid computing,big graph,scale free networks,scale-free networks,sparse matrices,eigenvalue equation,epidemic,vaccination campaigns,parallel iram algorithm,eigenvalue equation intervenes,infectious disease propagation,health care organizations,iram,epidemic spread modeling,compute pagerank,damping factor,big graph handling,complex networks,parallel algorithms,modeling epidemic spread,epidemics,pagerank,page rank matrix,pagerank matrix,dominant eigenvalue,graph theory,stochastic model,largest eigenvalue,yahoo,twitter,medical computing,page rank,eigenvalues and eigenfunctions,power law,vaccination strategy,efficient vaccination strategy,pagerank-like infection vector,network theory (graphs),probability,implicitly restarted arnoldi method | Graph theory,PageRank,Parallel algorithm,Computer science,Parallel computing,Algorithm,Theoretical computer science,Scale-free network,Stochastic modelling,Complex network,Eigenvalues and eigenvectors,Sparse matrix | Conference |
ISSN | ISBN | Citations |
1550-6533 | 978-1-4799-2927-6 | 2 |
PageRank | References | Authors |
0.37 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zifan Liu | 1 | 4 | 1.12 |
| Nahid Emad | 2 | 54 | 11.62 |
| Soufian Ben Amor | 3 | 26 | 7.99 |
| Michel Lamure | 4 | 13 | 7.00 |