walkscan

This repository provides a reference implementation of the local community detection algorithms described in the following research paper submitted to an upcomming conference:

Improving PageRank for Local Community Detection, Alexandre Hollocou, Thomas Bonald, Marc Lelarge

ArXiv link: https://arxiv.org/abs/1610.08722

C++

A C++ implementation of WalkSCAN, PageRank and LexRank is available in the c_code directory.

Compilation

Execute the following commands in the c_code directory:

mkdir build
cd build
cmake ..
make

Note: For MacOS X, install GCC with Homebrew and use:

cmake -DCMAKE_CXX_COMPILER=/usr/local/Cellar/gcc/6.2.0/bin/g++-6 ..

Usage for local community detection

To detect communities from given seed sets, use the walkscan command:

Usage: walkscan <flags>
Availaible flags:
    -i [graph file name] : Specifies the graph file (tab-separated list of edges).
    -o [output path/prefix] : Specifies the prefix for output files.
    -s [seed file name] : Specifies a file with the seed sets.
    -t [walk length] : Specifies the length of the random walks (default value: 2).
    -a [algorithm] : Specifies the algorithm (default value: 0).
        0: PageRank with conductance
        1: LexRank with conductance
        2: WalkScan (use flags --epsilon and --min-elems to change parameter values)

Usage for benchmarks

To perform benchmarks with ground-truth information, use the benchmarks command:

Usage: benchmarks <flags>
Availaible flags:
    -i [graph file name] : Specifies the graph file (tab-separated list of edges).
    -c [community file name] : Specifies the file with the ground-truth communities (tab-separated list of nodes).
    -b [benchmark type] :
        0 -> Classic benchmark: we pick a seed set in each ground-truth community.
        1 -> Random seed set: we pick k random seed nodes in the graph (and we make x simulations).
        2 -> Locally random seed set: we pick one seed set in the neighborhood of each ground-truth community.

Classic benchmark

Usage: benchmarks <flags> - CLASSIC BENCHMARK
Benchmark flags:
    -o [output path/prefix] : Specifies the prefix for output files.
    -s [seed file name] : Specifies a file with the seed sets (if not specified, seed nodes are chosen at random in each ground-truth community).
    -t [walk length] : Specifies the length of the random walks (default value: 2).
    -a [algorithm] : Specifies the algorithm (default value: 0).
        0: PageRank
        1: LexRank
        2: WalkScan (use flags --epsilon and --min-elems to change parameter values)
    -f [objective function] : Specifies the objective function for the sweep algorithm of PageRank or LexRank (default value: 0).
        0: Max-F1
        1: Rank Threshold
        2: Conductance

Random seed set benchmark

Usage: benchmarks <flags> - RANDOM SEED SET
Benchmark flags:
    -o [output path/prefix] : Specifies the prefix for output files.
    -s [seed file name] : Specifies a file with the seed sets (if not specified, k random seeds will be picked for each simulation).
    -k [number of seeds] : Specifies the number of seeds that will be picked at random at each simulation.
    -x [number of simulations]: Specifies the number of simulations.
    -t [walk length] : Specifies the length of the random walks (default value: 2).
    -a [algorithm] : Specifies the algorithm(default value: 0).
        0: PageRank (with conductance as objective function)
        2: WalkScan (use flags --epsilon and --min-elems to change parameter values)

Locally random seed set benchmark

Usage: benchmarks <flags> - LOCALLY RANDOM SEED SET
Benchmark flags:
    -o [output path/prefix] : Specifies the prefix for output files.
    -s [seed file name] : Specifies a file with the seed sets (if not specified, seeds are chosen at random in the neighborhood of ground-truth communities).
    -d [distance to ground-truth] : Specifies the maximum distance from the ground truth communities where seed nodes can be picked.
    -t [walk length] : Specifies the length of the random walks (default value: 2).
    -a [algorithm] : Specifies the algorithm (default value: 0).
        0: PageRank
        1: LexRank
        2: WalkScan (use flags --epsilon and --min-elems to change parameter values)
    -f [objective function] : Specifies the objective function for the sweep algorithm of PageRank or LexRank (default value: 0).
        0: Max-F1
        1: Rank Threshold
        2: Conductance

Python

An implementation of WalkSCAN in Python is available in the python_code directory.

Dependencies

WalkSCAN requires the following packages: NetworkX , NumPy and Scikit Learn.

Example

from walkscan import WalkSCAN
import networkx as nx

# Create a random graph with two partitions
G = nx.random_partition_graph([10, 15], 0.9, 0.01)

# Create a WalkSCAN instance
ws = WalkSCAN(nb_steps=3, eps=0.05, min_samples=2)

# Initialization vector for the random walk
init_vector = {0: 0.5, 1: 0.5}

# Compute the communities
ws.detect_communities(G, init_vector)

# Print the best community
print ws.communities_[0]

Usage

The class constructor of WalkSCAN takes three parameters:

• the length of the random walk: nb_steps
• the two parameters for DBSCAN eps and min_samples

In order to run the community detection algorithm, use the detect_communities method with parameters:

• graph: NetworkX graph (can be weighted)
• init_vector: dictionary node_id -> initial_probability to initialize the random walk

The results of the algorithm are stored in the attributes:

• communities_: list of communities, ordered by decreasing closeness to the seed set
• embedded_nodes_: list of the embedded nodes
• embedded_values_: dictionary node_id -> embedding_value containing the result of the embedding
• cores_: dictionary containing the cores computed via DBSCAN
• outliers_: list of the outliers computed by DBSCAN

Citing

If you find WalkSCAN interesting for your research, please consider citing our paper.

Contact

Please send any questions you might have about the paper or the code to [email protected].