Metadata-Version: 2.1
Name: twittertennis
Version: 0.1.2
Summary: Utility packages for Twitter tennis tournaments data sets.
Home-page: https://github.com/ferencberes/twittertennis
Author: Ferenc Beres
Author-email: fberes@info.ilab.sztaki.hu
License: UNKNOWN
Description: twittertennis
        ==============
        
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        Utility python package for RG17 and UO17 Twitter tennis tournament data sets.
        
        # Introduction
        
        This 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 that **the nodes (Twitter accounts) of the network are temporally labeled** thus we could compare online graph algortihms in **supervised evaluation** tasks. 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 our [paper](https://appliednetsci.springeropen.com/articles/10.1007/s41109-018-0080-5): Temporal walk based centrality metric for graph streams.
        
        # How to deploy?
        
        ## Install
        
        ```bash
        pip install twittertennis
        ```
        
        ## Tests
        
        ```
        git clone https://github.com/ferencberes/twittertennis.git
        cd twittertennis
        python setup.py test
        ```
        
        # Examples
        
        ## Quick start
        
        In this short example the RG17 (Roland-Garros 2017) data set is processed by the *TennisDataHandler* object. **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. 
        
        ```python
        import twittertennis.handler as tt
        
        handler = 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:
        ```python
        handler.export_relevance_labels(YOUR_OUTPUT_DIR, binary=True, only_pos_label=True)
        ```
        
        See more examples and information about the data in this [notebook](./examples/FirstLook.ipynb).
        
        # Related research
        
        **1. Temporal walk based centrality metric for graph streams:** [paper](https://appliednetsci.springeropen.com/articles/10.1007/s41109-018-0080-5) [code](https://github.com/ferencberes/online-centrality)
        
        ```
        @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:** [paper](https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0169-5) [code](https://github.com/ferencberes/online-node2vec)
        ```
        @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:** [paper](https://arxiv.org/abs/2104.07788) [code](https://github.com/benedekrozemberczki/pytorch_geometric_temporal)
        ```
        @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},
        }
        ```
        
Keywords: graph,dynamic graph,temporal network,mention graph,twitter
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
