C++ implementation of "On Multi-Query Local Community Detection"
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MQLC.h
MRW_main.cpp
Matrix.cpp
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README.md
graph.cpp
graph.h
heapSort.cpp
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tools.h

README.md

MQLC

C++ implementation of "On Multi-Query Local Community Detection"

First, please compile source code files in the folder "source_code" (We tested the code under Cygwin). For example,

g++ ./source_code/*.cpp -o MRW

The output executable file is MRW.exe (under Windows platform).

You can either directly execute MRW.exe (commond in ./MRW) and follow instructions to run a demo or run MRW with command. For example:

./MRW -i ./graph/com-amazon.ungraph.txt -o ./ -s ./graph/demo_seeds.txt -a 0.1 -b 0.6 -k 5 -g 0.3

Parameters:

-i InputFileNameEdge -s InputFileNameSeeds -o OutputFolder: default: current folder -a alpha -b beta -g gamma -k K

For the input files:

  1. InputFileNameEdge: edge file

MRW supports both weighted and unweighted graphs.

For weighted graph, the input format for an edge is "source_node target_node weight" (delimiter is "\t"). For example:

source_node target_node weight 1 88160 1.3 22 118052 2.1 35 161555 7.5 14 244916 0.5 25 346495 3.8

For unweighted graph, the input format for an edge is "source_node target_node" (delimiter is "\t"). For example:

source_node target_node 1 88160 22 118052 35 161555 14 244916 25 346495

  1. InputFileNameSeeds: query nodes set

Please list the query nodes in a row delimited with "\t" or in a column. For example:

Row: 201574 42687 161938 201570

Column: 201574 42687 161938 201570

Reference

@inproceedings{bian2018on, title={On Multi-Query Local Community Detection}, author={Bian, Yuchen and Yan, Yaowei and Cheng, Wei and Wang, Wei, and Luo, Dongheng, and Zhang, Xiang}, booktitle={Data Mining (ICDM), 2018 IEEE International Conference on}, year={2018}, organization={IEEE} }