VandinLab / SILVAN

Repo for the SILVAN paper

SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds

This repository contains the implementation of SILVAN, supporting our paper "SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds".

SILVAN is an algorithm to compute approximations of the Betweenness Centralities from graphs with progressive sampling. More details can be found in the paper, published in ACM Transactions on Knowledge Discovery from Data: https://dl.acm.org/doi/10.1145/3628601

Part of the underlying implementation of SILVAN is based on the sampling algorithm KADABRA by Michele Borassi and Emanuele Natale. Therefore, it has the same compilation dependencies (described below) and it is distributed with the same license (Apache License 2.0).

Installation

The software requires the OpenMP API. After cloning this repository, build the software by issuing the make command inside the silvan folder.

Running SILVAN

The network has to be provided as a file containing two space-separated integers u v per line, for each edge (u,v) of the network. The labels of the vertices are assumed to be consecutive.

To run SILVAN, you can use the run_silvan.py python script found in the scripts folder. It takes the following input parameters:

usage: run_silvan.py [-h] [-db DB] [-e EPSILON] [-d DELTA] [-a AEMPPEELING]
                     [-s ALPHA] [-k K] [-mh MH] [-eempp EEMPP] [-o O]
                     [-t TYPE]
optional arguments:
  -h, --help            show this help message and exit
  -db DB                path to graph input file
  -e EPSILON, --epsilon EPSILON
                        approximation accuracy parameter (in (0,1))
  -d DELTA, --delta DELTA
                        approximation confidence parameter (in (0,1), def. 0.1)
  -a AEMPPEELING, --aemppeeling AEMPPEELING
                        parameter a for empirical peeling (def. = 2)
  -s ALPHA, --alpha ALPHA
                        parameter alpha for sampling shortest paths (def. = 2.3)
  -k K                  parameter for top-k approximation
  -mh MH                enable computation of m_hat (def.=1)
  -eempp EEMPP          enable empirical peeling (def.=1)
  -o O                  output path (def.=results_silvan.csv or results_silvan_topk.csv)
  -t TYPE, --type TYPE  type of graph. 1 means directed, 0 undirected (def. undirected)

For example, to approximate the betweenness centrality of all nodes of the undirected graph graph.txt with absolute accuracy 0.01 and with probability at least 0.95, you can use

python run_silvan.py -db graph.txt -e 0.01 -d 0.05

or to approximate the top-10 most central nodes of the directed graph digraph.txt with relative accuracy 0.1, you can use

python run_silvan.py -db digraph.txt -e 0.1 -k 10 -t 1

Reproducing experiments

To reproduce the experiments described in the paper, follow the instructions listed below.

1. First you need to compile the algorithms. You can do so with the command make within the folders kadabra (found inside the kadabra.zip archive) and silvan. The folder kadabra contains a copy of the code from https://github.com/natema/kadabra with minimal modifications (mainly to better parse some of its statistics).
2. Then, move to the script folder. Download all graphs using python download_ds.py. Graphs will be downloaded in the datasets folder. Run python preprocessing.py to preprocess the downloaded graphs. Then, use the script python setup_bavarian.py to automatically setup BAVARIAN.
3. To run experiments described in Section 5.1, run python run_experiments.py. Results will be appended in the files results_silvan.csv for SILVAN, and results_kadabra.csv for KADABRA. The ablation experiments of Section 5.1.4 can be ran with the command python run_experiments_ablation.py
4. To run experiments described in Section 5.2, run python run_experiments_topk.py. Results will be appended in the file results_silvan_topk.csv for SILVAN, and in results_kadabra_topk.csv for KADABRA.
5. To run all experiments for BAVARIAN, run python run_experiments_bavarian.py. Results will be appended in the file results_bavarian.csv.

Contacts

You can contact us at [e-mail] and [e-mail] for any questions and for reporting bugs.

Aknowledgments

We would like to thank the authors of KADABRA for making their code freely available; this truly helped us developing our algorithm.