A Gentle Introduction to Deep Learning for Graphs

By Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda. Neural Networks, 2019

326 citations recorded by the source corpus.

The adaptive processing of graph data is a long-standing research topic that has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is a tutorial introduction to the field of deep learning for graphs. It favors a consistent and progressive presentation of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. 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. Moreover, it introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. We complement the methodological exposition with a discussion of interesting research challenges and applications in the field.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2e3a86c4b8f6883b371f718eb0a35857a6bf9b95

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Citations in the corpus

Benchmarking Graph Neural Networks (2023, background): “Refer to recent survey papers for a comprehensive overview of GNN literature (Bronstein et al., 2017; Zhou et al., 2018; Battaglia et al., 2018; Wu et al., 2019; Bacciu et al., 2019).”
An Overview of Deep Semi-Supervised Learning (2020, methodology): “To solve these issues, a number of methods used deep neural networks based methods applied to graphs [184, 166, 8].”
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning (2020, background): “A natural and straightforward strategy for this is to replace each undirected arc in G(t) by two directed ones connecting the same nodes with opposite directions, resulting in a fully directed graph denoted as GD(t) [39, 40].”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “We refer the reader to recent publication [28] for a deeper analysis of spectral versus spatial GNNs, and [29], [53] for a comprehensive review of GNNs.”