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Andreas Roth and Thomas Liebig (Project B4) win Best Paper Award of the ECML-PKDD 2022

October 11, 2022 12:2

Andreas Roth presenting at ECML 2022

At this year's ECML-PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), Andreas Roth and Thomas Liebig (both SFB 876 - B4) received the "Best Paper Award". In their paper "Transforming PageRank into an Infinite-Depth Graph Neural Network" they addressed a weakness of graph neural networks (GNNs). In GNNs, graph convolutions are used to determine appropriate representations for nodes that are supposed to link node features to context within a graph. If graph convolutions are performed multiple times in succession, the individual nodes within the graph lose information instead of benefiting from increased complexity. Since PageRank itself exhibits a similar problem, a long-established variant of PageRank is transformed into a Graph Neural Network. The intuitive derivation brings both theoretical and empirical advantages over several variants that have been widely used so far.


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