# Geometric Deep Lean Learning: Evaluation Using a Twitter Social Network
## 1 Introduction  
Today, the fact that data are all around us appears to be almost a truism. According to recent studies, in the year 2025, humanity will create about 163 zettabytes of information [1]. The alarming aspect, however, is not that we will be overwhelmed by data, but that these data will be very different from what we are used to deal with in traditional disciplines such as signal or image processing, statistics, or automatic learning [2, 3]. Moreover, the data that we will face will emerge from the billions of objects connected to the Internet of Things [4, 5]. In an Industry 4.0 context, such as the industrial Internet of Things [6, 7], these data are produced by decentralised sources such as thousands of sensors in factories [8], i.e., the data are distributed over networks [9]. Therefore, there is a pressing need for societies to understand data distributed in complex networks to, among other considerations, make predictions about their behaviour, and this is the main motivation for this work. This work proposes an application that belongs to the emerging field of machine learning on graphs, which proceeds from algorithmic reasoning [10, 11], relational structure discovery [12, 13], or the application of dynamic graphs [14, 15] with multiple applications in fake account detection [16] or fraud detection [17]. The major challenge in this area is to find a way to depict or encode the structure of graphs so that it can be easily exploited by machine learning models. Within this field, geometric deep learning is an emerging technique to generalise deep learning models to non-Euclidean domains such as certain graphs and manifolds [18, 19, 20, 21, 22, 23], and has been previously used in graph-wise classification [24], signal processing [25], vertex-wise classification [26], or graph dynamics classification [18]. The main goal of this work is to evaluate a geometric deep learning algorithm for link prediction, which has previously been formulated theoretically by [PERSON] et al. [23],to be used in dynamic complex networks [18]. Scholars have shown how the evaluation of topic clusters through content-based social network analysis can be used to study the network evolution in terms of relevant topics within one type of node; however, these approaches typically need expert knowledge about the clusters under scrutiny [27; 28]. Methods based on direct use of deep learning techniques have been used for dynamic network link prediction with a combination of long short-term memory decoders [29], other scholars assigned time-varying link weights at different times and executed link prediction with convolutional network methods based on common neighbours [30]; however, researchers have demonstrated the inherent difficulty of training deep learning algorithms with sparse and heterogeneous data [31]. The distinguishing feature of the proposed geometric deep learning algorithm is that it can be applied to link prediction in dynamic complex networks without prior knowledge, by using the network structure and the messages sent by the network nodes. The contribution of this work is twofold. On the one hand, this work presents an evaluation dataset corresponding to a non-Euclidean network that has been created using Twitter messages. On the other hand, it presents the results of the evaluation procedure that uses this dataset to assess the geometric deep learning algorithm mentioned above. The evaluation results confirm the ability of the algorithm for link prediction in this type of network and present how the model accuracy can increase by using the content of messages between nodes. The results obtained in this work outperform the existing results on link prediction based on graph neural networks on datasets without the content of messages between nodes [32].