This is a supplementary code for the paper When Less is More: Systematic Analysis of Cascade-based Community Detection.
Supplementary directories:
CommDiff-Package/: the source code for R-CoDi and D-CoDi from the paper “Community Detection Using Diffusion Information” by Ramezani et al.
CommunityWithoutNetworkLight/: the source code for C-IC and C-Rate algorithms from the paper “Efficient methods for influence-based network-oblivious community detection” by Barbieri et al.
network-inference-multitree/: the source code for the MultiTree algorithm from the paper “Submodular inference of diffusion networks from multiple trees” by Gomez-Rodriguez et al.
community_ext/: community detection library complementing the paper “Community detection through likelihood optimization: in search of a sound model” by Prokhorenkova et al.
benchmark/: used to generate synthetic graphs according to the LFR model proposed in “Benchmark graphs for testing community detection algorithms” by Lancichinetti et al.
Datasets directories:
LFR_1000/, citeseer/, cora-small/, cora/, dolphins/, eu-core/, football/, karate/, newsgroup/, polblogs/, polbooks/, twitter/.
Directories with results: average_ranks/ and average_results/ contain aggregated results over real-world datasets, cascade_plots/ contains the distribution of cascade sizes.
Description of scripts:
C-SI-BD.py, SI-BD.py, SIR.py - to generate epidemics;
base_algorithms.py, base_algorithms_twitter.py - simple algorithms;
baseline_barbieri.py, baseline_barbieri_twitter.py - to run C-IC and C-Rate;
baseline_cd.py, baseline_cd_twitter.py - to run R-CoDi and D-CoDi;
opt_algorithms.py, opt_algorithms_twitter.py - GraphOpt and ClustOpt algorithms;
cascade_plots.py, cascade_plots_twitter.py - to generate data for plots;
average_rank.py, average_results.py to aggregate the results.
To reproduce the main experiments from the paper one can use the file paper_experiments.tex.