text: 265,156,1280482460510433281,1279570369184247810,Péter Mernyei,"Just published our work on Wiki-CS, a new node classification benchmark! Many existing datasets are structurally similar. Our benchmark provides more variety and raises new challenges. Paper: <LINK> Will present as a spotlight at the GRL+ workshop at #icml2020! <LINK> Compared to standard citation network datasets, this graph is much denser, with an average node degree of ~37 as opposed to ~4-5. The Deep Graph Mapper visualisation above also seems to indicate a more centralised, hierarchical structure. <LINK> There is a lot more inter-class connectivity: we calculated the share of each node's same-class neighbours, and plotted the distribution of this property for different datasets. There is a significant spread in Wiki-CS, most nodes are not in homogenous neighbourhoods. <LINK> This suggests that more complex methods for aggregating large neighbourhoods might be able to improve prediction accuracy. The work was the result of my final year undergraduate project at @Cambridge_CL, supervised by @catalinacangea. Thanks also to @PetarV_93 for inspiring my interest in this area!",https://arxiv.org/abs/2007.02901,"We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes corresponding to Computer Science articles, with edges based on hyperlinks and 10 classes representing different branches of the field. We use the dataset to evaluate semi-supervised node classification and single-relation link prediction models. Our experiments show that these methods perform well on a new domain, with structural properties different from earlier benchmarks. The dataset is publicly available, along with the implementation of the data pipeline and the benchmark experiments, at this https URL . ",Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks,5,"['Just published our work on Wiki-CS, a new node classification benchmark! Many existing datasets are structurally similar. Our benchmark provides more variety and raises new challenges.\nPaper: <LINK>\nWill present as a spotlight at the GRL+ workshop at #icml2020! <LINK>', 'Compared to standard citation network datasets, this graph is much denser, with an average node degree of ~37 as opposed to ~4-5. The Deep Graph Mapper visualisation above also seems to indicate a more centralised, hierarchical structure. https://t.co/boSESmdxjI', ""There is a lot more inter-class connectivity: we calculated the share of each node's same-class neighbours, and plotted the distribution of this property for different datasets. There is a significant spread in Wiki-CS, most nodes are not in homogenous neighbourhoods. https://t.co/Qusn4PD0JS"", 'This suggests that more complex methods for aggregating large neighbourhoods might be able to improve prediction accuracy.', 'The work was the result of my final year undergraduate project at @Cambridge_CL, supervised by @catalinacangea. Thanks also to @PetarV_93 for inspiring my interest in this area!']",20,07,1091