Several research outcomes have been implemented and integrated in pygrank.
In addition to the package itself [krasanakis2022pygrank], please cite related publications, for example
by modifying automatically generated descriptions (see below). Do not forget to also cite dataset sources! Related instructions
can be found here.
The NodeRanking.cite() method can be used to
automatically generate descriptions of algorithms that include
references. Reference names correspond to the list of
publication bibtex entries presented in the rest of the document.
For example, the following snippet defines a node ranking algorithm and retrieves a textual description to be pasted in LaTeX documents after adding respective publications to the bibliography.
>>> import pygrank as pg
>>> algorithm = pg.BoostedSeedOversampling(pg.PageRank())
>>> print(algorithm.cite())
personalized PageRank \cite{page1999pagerank} with restart probability 0.15 and iterative partial boosted seed oversampling of previous node scores \cite{krasanakis2019boosted} postprocessorPublications that have supported development of various aspects of this library. These are presented in reverse chronological order.
@article{krasanakis2022pygrank,
author = {Emmanouil Krasanakis, Symeon Papadopoulos, Ioannis Kompatsiaris, Andreas Symeonidis},
title = {pygrank: A Python Package for Graph Node Ranking},
journal = {SoftwareX},
year = 2022,
month = oct,
doi = {10.1016/j.softx.2022.101227},
url = {https://doi.org/10.1016/j.softx.2022.101227}
}
@article{krasanakis2022fast,
title={Fast Library Recommendation in Software Dependency Graphs with Symmetric Partially Absorbing Random Walks},
author={Krasanakis, Emmanouil and Symeonidis, Andreas},
journal={Future Internet},
volume={14},
number={5},
pages={124},
year={2022},
publisher={Multidisciplinary Digital Publishing Institute}
}
@article{krasanakis2020unsupervised,
title={Unsupervised evaluation of multiple node ranks by reconstructing local structures},
author={Krasanakis, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Yiannis},
journal={Applied Network Science},
volume={5},
number={1},
pages={1--32},
year={2020},
publisher={Springer}
}
@article{krasanakis2019boosted,
title={Boosted seed oversampling for local community ranking},
author={Krasanakis, Emmanouil and Schinas, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Yiannis and Symeonidis, Andreas},
journal={Information Processing \& Management},
pages={102053},
year={2019},
publisher={Elsevier}
}
@inproceedings{krasanakis2022autogf,
title={AutoGF: Runtime Graph Filter Tuning for Community Node Ranking},
author={Krasanakis, Emmanouiland Papadopoulos, Symeon and Kompatsiaris, Ioannis},
year={2022},
booktitle={Complex Networks},
}
@inproceedings{krasanakis2020stopping,
title={Stopping Personalized PageRank without an Error Tolerance Parameter},
author={Krasanakis, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Ioannis},
year={2020},
booktitle={ASONAM},
}
@inproceedings{krasanakis2020fairconstr,
title={Applying Fairness Constraints on Graph Node Ranks under Personalization Bias},
author={Krasanakis, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Ioannis},
year={2020},
booktitle={Complex Networks},
}
@inproceedings{krasanakis2019linkauc,
title={LinkAUC: Unsupervised Evaluation of Multiple Network Node Ranks Using Link Prediction},
author={Krasanakis, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Yiannis},
booktitle={International Conference on Complex Networks and Their Applications},
pages={3--14},
year={2019},
organization={Springer}
}
@inproceedings{krasanakis2018venuerank,
title={VenueRank: Identifying Venues that Contribute to Artist Popularity},
author={Krasanakis, Emmanouil and Schinas, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Yiannis and Mitkas, Pericles A},
booktitle={ISMIR},
pages={702--708},
year={2018}
}
@article{krasanakis2020prioredit,
title={Prior Signal Editing for Graph Filter Posterior Fairness Constraints},
author={Krasanakis, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Ioannis and Symeonidis, Andreas},
journal={arXiv:2108.12397},
year={2021}
}
Additional publications introducing methods implemented in this package.
@inproceedings{yu2021chebyshev,
title={Chebyshev Accelerated Spectral Clustering},
author={Yu, Tianyu and Zhao, Yonghua and Huang, Rongfeng and Liu, Shifang and Zhang, Xinyin},
booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
pages={247--255},
year={2021}
}
@article{tsioutsiouliklis2020fairness,
title={Fairness-Aware Link Analysis},
author={Tsioutsiouliklis, Sotiris and Pitoura, Evaggelia and Tsaparas, Panayiotis and Kleftakis, Ilias and Mamoulis, Nikos},
journal={arXiv preprint arXiv:2005.14431},
year={2020}
}
@inproceedings{rahman2019fairwalk,
title={Fairwalk: Towards Fair Graph Embedding.},
author={Rahman, Tahleen A and Surma, Bartlomiej and Backes, Michael and Zhang, Yang},
booktitle={IJCAI},
pages={3289--3295},
year={2019}
}
@article{ortega2018graph,
title={Graph signal processing: Overview, challenges, and applications},
author={Ortega, Antonio and Frossard, Pascal and Kova{\v{c}}evi{\'c}, Jelena and Moura, Jos{\'e} MF and Vandergheynst, Pierre},
journal={Proceedings of the IEEE},
volume={106},
number={5},
pages={808--828},
year={2018},
publisher={IEEE}
}
@article{klicpera2018predict,
title={Predict then propagate: Graph neural networks meet personalized pagerank},
author={Klicpera, Johannes and Bojchevski, Aleksandar and G{\"u}nnemann, Stephan},
journal={arXiv preprint arXiv:1810.05997},
year={2018}
}
@article{susnjara2015accelerated,
title={Accelerated filtering on graphs using lanczos method},
author={Susnjara, Ana and Perraudin, Nathanael and Kressner, Daniel and Vandergheynst, Pierre},
journal={arXiv preprint arXiv:1509.04537},
year={2015}
}
@article{susnjara2015accelerated,
title={Accelerated filtering on graphs using lanczos method},
author={Susnjara, Ana and Perraudin, Nathanael and Kressner, Daniel and Vandergheynst, Pierre},
journal={arXiv preprint arXiv:1509.04537},
year={2015}
}
@inproceedings{wu2012learning,
title={Learning with Partially Absorbing Random Walks.},
author={Wu, Xiao-Ming and Li, Zhenguo and So, Anthony Man-Cho and Wright, John and Chang, Shih-Fu},
booktitle={NIPS},
volume={25},
pages={3077--3085},
year={2012}
}
@inproceedings{li2011link,
title={Link prediction: the power of maximal entropy random walk},
author={Li, Rong-Hua and Yu, Jeffrey Xu and Liu, Jianquan},
booktitle={Proceedings of the 20th ACM international conference on Information and knowledge management},
pages={1147--1156},
year={2011}
}
@inproceedings{scellato2011exploiting,
title={Exploiting place features in link prediction on location-based social networks},
author={Scellato, Salvatore and Noulas, Anastasios and Mascolo, Cecilia},
booktitle={Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining},
pages={1046--1054},
year={2011}
}
@article{chung2007heat,
title={The heat kernel as the pagerank of a graph},
author={Chung, Fan},
journal={Proceedings of the National Academy of Sciences},
volume={104},
number={50},
pages={19735--19740},
year={2007},
publisher={National Acad Sciences}
}
@inproceedings{andersen2007local,
title={Local partitioning for directed graphs using PageRank},
author={Andersen, Reid and Chung, Fan and Lang, Kevin},
booktitle={International Workshop on Algorithms and Models for the Web-Graph},
pages={166--178},
year={2007},
organization={Springer}
}
@techreport{page1999pagerank,
title={The PageRank citation ranking: Bringing order to the web.},
author={Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry},
year={1999},
institution={Stanford InfoLab}
}
To match the library's terminology with literature concepts, here is a glossary of common concepts sorted alphabetically:
| Term | Explanation |
|---|---|
| Graph filter | A strategy of diffusing node scores through graphs through edges. |
| Graph neural network | A combination of traditional neural networks and node ranking algorithms. |
| Graph signal | Scores assigned to nodes. Each node's score typically assumes values in the range [0,1]. |
| Node ranking algorithm | An algorithm that starts with a graph and personalization and outputs graph signals that boast higher quality than the personalization, for example by making predictions for all graph ndes. Typically consists of combinations of graph filters with postprocessing schemes. |
| Personalization | The graph signal inputted in grap filters. This is also known as graph signal priors or the personalization vector. |
| Seeds | Example nodes that are known to belong to a community. |
| Tuning | A process of determining algorithm hyper-parameters that optimize some evaluation measure. |