Federico Errica

TL;DR: By comparing GNNs with structure-agnostic baselines the authors provide convincing evidence that, on some datasets, structural information has not been exploited yet and can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.

TL;DR: The paper takes a top-down view of the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing and introduces the basic building blocks that can be combined to design novel and effective neural models for graphs.

TL;DR: The Contextual Graph Markov Model (CGMM) as discussed by the authors is an approach combining ideas from generative models and neural networks for the processing of graph data, which is based on a constructive methodology to build a deep architecture comprising layers of probabilistic models.

TL;DR: In this article, the authors provide an overview of common practices that should be avoided to fairly compare with the state-of-the-art graph representation learning models, by running more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks.

TL;DR: The Contextual Graph Markov Model is introduced, an approach combining ideas from generative models and neural networks for the processing of graph data to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion.