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On Multi-Query Local Community Detection

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

Please refer to the following paper for details.

Reference:

On Multi-Query Local Community Detection
Bian, Yuchen and Yan, Yaowei and Cheng, Wei and Wang, Wei, and Luo, Dongheng, and Zhang, Xiang
IEEE International Conference on Data Mining (ICDM),2018

Compile

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).

Running

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

Input

  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

Citing

@inproceedings{bian2018multi,
  title={On Multi-query Local Community Detection},
  author={Bian, Yuchen and Yan, Yaowei and Cheng, Wei and Wang, Wei and Luo, Dongsheng and Zhang, Xiang},
  booktitle={2018 IEEE International Conference on Data Mining (ICDM)},
  pages={9--18},
  year={2018},
  organization={IEEE}
}

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