This project is for the simulation of random walks on different types of random graphs, including ER network, WS network, scale free network such as flower network, Sierpinski network, and real life network including Facebook Ego network and a sub Web graph (subsampling using forestfire algorithm). The statistics we are interested is mean first passage time(MFPT). Full results (.mat with .fig) can be downloaded here.
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AdjMat2D3Graph: visualization of graph using d3.js
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Main: major simulation code in this folder
- meanfstpsg.m: given a Markov chain simulation with (Transition Probability Matrix, Minimum Distance Matrix, Minimum number of times every node should be visited), return MFPT
- mfpsimulatealpha/mu/alpha-multiworkers/mu-multiworkers.m: get relation of MFPT with mu and alpha through simulations
- betamu/alpha.m: comparision of theoretical results with simulation results about relation MFPT(mu) and relation MFPT(alpha).
- ffsampling.m: implementation of forest fire graph sampling algorithm
- task.sh: make life easier
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UVnetwork/UVnet.m: generator of flower graph model
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batchtsks/simulate.batch: sbatch script
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crawlernet/stanford/fbego/txt2mat.py: convert mfpsimulatemu-multiworkers.m results to mat
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crawlernet, networkbase: store all type of networks' adjency matrix and MFPT simulation results