Package: twittertennis
twittertennisUtility python package for RG17 and UO17 Twitter tennis tournament data sets.IntroductionThis repository is a Python package that ease the interaction with two Twitter data sets related to tennis tournaments: RG17 (Roland-Garros 2017) and UO17 (USOpen 2017). In our research, we used the underlying Twitter mention graphs to analyse the performance of mulitple dynamic centrality measures and temporal node embedding methods. A huge advantage of our data is thatthe nodes (Twitter accounts) of the network are temporally labeledthus we could compare online graph algortihms insupervised evaluationtasks. The labels encode whether a given node in the Twitter mention network is related to a tennis player who played in a tournament on the given day. For more details on these data sets, see ourpaper: Temporal walk based centrality metric for graph streams.How to deploy?InstallpipinstalltwittertennisTestsgit clone https://github.com/ferencberes/twittertennis.git
cd twittertennis
python setup.py testExamplesQuick startIn this short example the RG17 (Roland-Garros 2017) data set is processed by theTennisDataHandlerobject.The data is automatically downloaded to the '../data/' folder during the first execution!After (downloading and) processing the data, daily node relevance labels are exported and prepared for further analysis.importtwittertennis.handlerastthandler=tt.TennisDataHandler("../data/","rg17",include_qualifiers=True)print(handler.summary())handler.export_relevance_labels(YOUR_OUTPUT_DIR,binary=True)OR change the last line of the code if you only want to export relevant nodes for each day:handler.export_relevance_labels(YOUR_OUTPUT_DIR,binary=True,only_pos_label=True)See more examples and information about the data in thisnotebook.Related research1. Temporal walk based centrality metric for graph streams:papercode@article{Beres2018,
author="B{\'e}res, Ferenc
and P{\'a}lovics, R{\'o}bert
and Ol{\'a}h, Anna
and Bencz{\'u}r, Andr{\'a}s A.",
title="Temporal walk based centrality metric for graph streams",
journal="Applied Network Science",
year="2018",
volume="3",
number="32",
pages="26",
issn="2364-8228",
}2. Node embeddings in dynamic graphs:papercode@Article{Béres2019,
author="B{\'e}res, Ferenc
and Kelen, Domokos M.
and P{\'a}lovics, R{\'o}bert
and Bencz{\'u}r, Andr{\'a}s A.",
title="Node embeddings in dynamic graphs",
journal="Applied Network Science",
year="2019",
volume="4",
number="64",
pages="25",
}3. PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models:papercode@article{RozemberczkiPGT2021,
  author    = {Benedek Rozemberczki and
               Paul Scherer and
               Yixuan He and
               George Panagopoulos and
               Maria Sinziana Astefanoaei and
               Oliver Kiss and
               Ferenc B{\'{e}}res and
               Nicolas Collignon and
               Rik Sarkar},
  title     = {PyTorch Geometric Temporal: Spatiotemporal Signal Processing with
               Neural Machine Learning Models},
  volume    = {abs/2104.07788},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.07788},
  archivePrefix = {arXiv},
  eprint    = {2104.07788},
}