# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 5 Code Repositories  
When performing research in a new field, it is important for researchers to have access to useful and reusable resources. This includes datasets, as well as code, model and algorithm implementations, that can be employed. Luckily, in the case of GCNs, there are many resources, and in the following, we try to compile a list of the most useful code repositories and their content. Authors in [36] provide a link to their code repository ([[https://github.com/shakshi1](https://github.com/shakshi1)]([https://github.com/shakshi1](https://github.com/shakshi1)) 2/Rumor-Spreaders-using-GNN-approach-PHEME-dataset-, accessed on 17 February 2022), which employs NLTK ([[https://www.nltk.org/](https://www.nltk.org/)]([https://www.nltk.org/](https://www.nltk.org/)), accessed on 17 February 2022) for pre-processing the English tweets and extracting their sentiment and Gensim ([[https://radimrehurek.com/gensim/](https://radimrehurek.com/gensim/)]([https://radimrehurek.com/gensim/](https://radimrehurek.com/gensim/)), accessed on 17 February 2022 to obtain word embeddings. They also employ the NetworkX ([[https://networkx.org/](https://networkx.org/)]([https://networkx.org/](https://networkx.org/)), accessed on 17 February 2022) library to construct the graph and the Spektral ([[https://graphneural.network/](https://graphneural.network/)]([https://graphneural.network/](https://graphneural.network/)), accessed on 17 February 2022) python library to perform the Graph Convolution. Authors in [29] provide a link to a useful code repository ([[https://github.com/nikhilmaram/Show_and_Tell](https://github.com/nikhilmaram/Show_and_Tell)]([https://github.com/nikhilmaram/Show_and_Tell](https://github.com/nikhilmaram/Show_and_Tell)), accessed on 17 February 2022) for extracting visual features from images with the image2 sentence model, whereas in [33], the authors share the code and data for Twitter bot detection using GCNs on github ([[https://github.com/slab-itu/altmetrics_bot](https://github.com/slab-itu/altmetrics_bot)]([https://github.com/slab-itu/altmetrics_bot](https://github.com/slab-itu/altmetrics_bot)), accessed on 17 February 2022). Their python code employs NetworkX for graph creation and a TensorFlow implementation of the Graph Convolution Layer. The work performed in [34] can be replicated using the shared data and code ([[https://github.com/shanmon110/BGSRD](https://github.com/shanmon110/BGSRD)]([https://github.com/shanmon110/BGSRD](https://github.com/shanmon110/BGSRD)), accessed on 17 February 2022), which employs the BERT model to convert documents into a heterogeneous graph that contains both documents and words as nodes and edges that correspond to the global word co-occurrence (i.e., PMI) in the corpus. In the context of rumor detection, code ([[https://github.com/jihochoi/dynamic-gcn](https://github.com/jihochoi/dynamic-gcn)]([https://github.com/jihochoi/dynamic-gcn](https://github.com/jihochoi/dynamic-gcn)), accessed on 17 February 2022) and data ([[https://figshare.com/s/d8984](https://figshare.com/s/d8984) fd39557a3d295e8]([https://figshare.com/s/d8984](https://figshare.com/s/d8984) fd39557a3d295e8), accessed on 17 February 2022) are provided by [40]. The code for the Dynamic GCNs is developed on PyTorch Geometric, and several execution parameters allow trying different snapshot numbers, attention modules and datasets. When it comes down to evolving graphs and handling graph snapshots [59], the EvolveGCN ([[https://github.com/IBM/EvolveGCN](https://github.com/IBM/EvolveGCN)]([https://github.com/IBM/EvolveGCN](https://github.com/IBM/EvolveGCN)), accessed on 17 February 2022) code can be modified to capture the evolution of the rumor spreading graph.