Guest editorial: Special issue on information visualisation

In the current information era, most aspects of life depend on and driven by data, information, knowledge and user experience. The infrastructure of an information-dependent society and drive for new innovation and direction of activities heavily relies on the quality of data, information and analysis of such entities from past to its projected future activities. Information Visualisation, Visual Analytics, Business Intelligence, machine learning and application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces

Are Graph Convolutional Networks Fully Exploiting Graph Structure?

Graph Convolutional Networks (GCNs) generalize the idea of deep convolutional networks to graphs, and achieve state-of-the-art results on many graph related tasks. GCNs rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours. In this paper we formalize four levels of structural information injection, and use them to show that GCNs ignore important long-range dependencies embedded in the overall topology of a graph. Our proposal includes a novel regularization technique based on random walks with restart, called RWRReg, which encourages the network to encode long-range information into the node embeddings. RWRReg is further supported by our theoretical analysis, which demonstrates that random walks with restart empower aggregation-based strategies (i.e., the Weisfeiler-Leman algorithm) with long-range information. We conduct an extensive experimental analysis studying the change in performance of several state-of-the-art models given by the four levels of structural information injection, on both transductive and inductive tasks. The results show that the lack of long-range structural information greatly affects performance on all considered models, and that the information extracted by random walks with restart, and exploited by RWRReg, gives an average accuracy improvement of more than $5\%$ on all considered tasks