# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 6 Discussion
The GC layers are followed by pooling layers that coarse the graph into sub-graphs, with nodes that represent higher graph-level representations. A readout layer with a mean, max, sum or any other aggregation function allows summarizing the final node representations in a single representation for the whole graph. This allows classifying the whole graph (which may correspond to a text and its context, a user and their neighbors and profiles or a rumor propagation graph) to one of many class labels using multiple dense layer and a softmax layer at the end. It is possible to compare approaches that employ different techniques when they are evaluated on the same dataset and the same task. For example, the KMGCN proposed in [27] achieved an accuracy of 0.886 on the Weibo dataset, whereas the KMAGCN presented in [29] using a pre-trained XLnet improved the accuracy to 0.944. This improvement is mainly due to the use of visual features, which are captured using VGG-19. On the other side, the heterogeneous graph in [30] allows the HDGCN method to achieve an accuracy as high as 0.961 on the same dataset. Experiments on the Weibo dataset for the detection of rumors also demonstrated the ability of GCNs to predict such cases early. More specifically, the combined use of two GCNs (i.e., one for the propagation and one for the dispersion graph) in the Bi-GCN early stopping method achieves a prediction accuracy of 0.961, as reported in [38]. The same model has been evaluated in [40] on the same dataset using a different train-validation-test split and 10 runs, and the reported average accuracy for Bi-GCN was 0.928. In addition, the proposed early stopping method of this work, DynGCN, improved the accuracy to 0.936 in the same experimental setup. The pattern of results is the same in both papers for the Twitter 15 and Twitter 16 datasets. The main advantage of DynGCN over the Bi-GCN is that it trains a different GCN for each snapshot of the rumor diffusion graph and addsan attention layer that weights the importance of the graph snapshot embeddings. Both additive and dot-product attention mechanisms have been tested with the additive model to provide slightly better results in all datasets. Another straight comparison of the various methods can be performed on the PHEME dataset. In [27], the authors report an accuracy of 0.8756 with their best variant of KMGCN, whereas the authors in [29] report a slightly worse accuracy of 0.867 for the KMAGCN method using BERT. The same dataset has been used in [36; 41] but for a completely different task, which is the identification of rumor spreaders. Both works have employed specific subsets of the dataset that refer to breaking news and, more specifically, the Charlie Hebdo shooting, Ferguson unrest, Ottawa shooting, Sydney hostage crisis and Germanwings crash. The GCN method used in [36] achieved an accuracy of 0.790, 0.705, 0.675, 0.655 and 0.715, respectively, in each subset. The User-Reply-GCN of [41] was tested on the same dataset but with a random oversampling technique that changed the percentage ratio between rumors and non-rumors. In this case, the authors reported an overall F1-score of 0.80 for the rumor class in all subsets and 0.79 for the non-rumor class. Compared to the approaches that use simple Convolutional Neural Networks, GCNs share the same intuition since they try to learn the relationships between neighboring nodes similarly to learning the relationships between neighboring pixels. However, CNNs rely on two-dimensional data (pixels) with a specific structure and positioning, whereas GCNs can work with any kind of arbitrary structured data (nodes). Recurrent Neural Networks can ideally be combined with GCNs in order to capture the evolution of dynamic graphs [59] and have several applications ranging from scene perception and action-recognition [60] to traffic prediction [61]. This combination is very promising in the case of fake news that propagate through social networks and have very recently been applied [40] in their detection task.