Joakim Skarding

Joakim Skarding, Pavel Sanda

The OpenAIRE graph contains a large citation graph dataset, with over 200 million publications and over 2 billion citations. The current graph is available as a dump with metadata which uncompressed totals ~TB. This makes it hard to process on conventional computers. To make this network more available for the community we provide a processed OpenAIRE graph which is downscaled to 32GB, while preserving the full graph structure. Apart from this we offer the processed data in very simple format, which allows further straightforward manipulation. We also provide a python pipeline, which can be used to process the next releases of the OpenAIRE graph.

Joakim Skarding, Bogdan Gabrys, Katarzyna Musial

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology