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README.txt
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README.txt
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This repository was created by Andrej Jurco, Paolo Sylos Labini and Flavio Vella.
The code implements algorithms for the calculation of the Expected Force metric on SNAP graphs in C++ and OpenMP.
It contains:
**original serial implementation (exffunction.cpp copyright Glenn Lawyer, 2013.)
**OpenMP implementation.
**CUDA GPU implementation (our efficient algorithm, see the code in the directory ``parallel`` )
--------------------------------------------------------------
USAGE via Makefile
"make all" will compile and test the code.
"make compile" will compile the code and create executable named ExpForce.
"make run_test" will run a test on the graph stored in "fb_full.txt" and produce a result file "fb_full_results.txt".
---------------------------------------------------------------
GENERAL USAGE
Once compiled, an executable named ExpForce should appear.
Execute it with any number of filenames as arguments;
example: OMP_NUM_THREADS=16 ./ExpForce fb_full.txt 1 fb_exp.score.txt, where fb_full.txt contains a full, sorted edgelists such as
0 2
1 2
2 0
2 1
-----------------------------------------------------------------
CONTENTS
exffunction.cpp is the Glenn Lawyer original function. Calculates the expected force of a node.
main.cpp loads a graph from a text file and calculate the expected force of the nodes.
stdafx.h is an header for standard libraries and the exfccp function.
fb_full.txt is a test graph.
**Reference**
@INPROCEEDINGS{10495558,
author={Labini, Paolo Sylos and Jurco, Andrej and Ceccarello, Matteo and Guarino, Stefano and Mastrostefano, Enrico and Vella, Flavio},
booktitle={2024 32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
title={Scaling Expected Force: Efficient Identification of Key Nodes in Network-Based Epidemic Models},
year={2024},
volume={},
number={},
pages={98-107},
keywords={Measurement;Epidemics;Force measurement;Scalability;Computational modeling;Force;Graphics processing units;Epidemic;SIR;Big Data;Expected Force;Graph Centrality;Network;Parallel Computing},
doi={10.1109/PDP62718.2024.00021}}