AttriRank - Unsupervised Ranking using Graph Structures and Node Attributes
Python C++
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AttriRank_inC C++ version of AttriRank Jan 24, 2017
sample add files Nov 5, 2016
tests flake8 Nov 5, 2016
.travis.yml travisCI setting file Nov 5, 2016 Update Nov 15, 2017
attrirank.pdf add paper pdf May 25, 2017
requirements.txt update requirements Nov 5, 2016


Build Status

AttriRank is an unsupervised ranking model that considers not only graph structure but also the attributes of nodes.

A reference implementation of AttriRank in the paper (please see the file - attrirank.pdf):

Unsupervised Ranking using Graph Structures and Node Attributes
Chin-Chi Hsu, Yi-An Lai, Wen-Hao Chen, Ming-Han Feng, and Shou-De Lin
Web Search and Data Mining (WSDM), 2017



Run AttriRank on sample graph with features, using damp [0.2, 0.5, 0.8]:

python src/ --damp 0.2 0.5 0.8 --inputgraph sample/graph.edgelist --inputfeature sample/graph.feature


Check out optional arguments such as AttriRank with prior, different similarity kernels by:

python src/ --help


Supported graph format is the edgelist:

node_from node_to

Supported feature format is the table (Comma-Separated Values):

node_i, feat_dim_1, feat_dim_2, ...

Default settings for graph are directed and unweighted.


A comma-separated table of ranking scores with columns: [node_id, damp1, damp2, ...]


where score_1 is the ranking score of node 0 using AttriRank with damp 0.2.


Install all dependencies:

pip install -r requirements.txt


If you find AttriRank useful in your research, please consider citing the paper:

     author = {Hsu, Chin-Chi and Lai, Yi-An and Chen, Wen-Hao and Feng, Ming-Han and Lin, Shou-De},
     title = {Unsupervised Ranking Using Graph Structures and Node Attributes},
     booktitle = {Proceedings of the Tenth ACM International Conference on Web Search and Data Mining},
     series = {WSDM '17},
     year = {2017},


If having any questions about the paper, please contact us at
If having any questions about codes, please contact us at