A Gentle Introduction to Deep Learning for Graphs

Abstract

The adaptive processing of graph data is a long-standing research topic which 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 designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction 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 to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field.

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• Transition Propagation Graph Neural Networks for Temporal Networks
• Explaining Deep Graph Networks with Molecular Counterfactuals
• Addressing Heterophily in Node Classification with Graph Echo State Networks
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• Some open questions on morphological operators and representations in the deep learning era
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• Low Complexity Approaches for End-to-End Latency Prediction
• Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
• Generalizing Downsampling from Regular Data to Graphs
• Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
• Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling
• Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness
• Graph Mixture Density Networks
• Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation
• Beyond Homophily with Graph Echo State Networks
• Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
• Modeling Edge Features with Deep Bayesian Graph Networks
• Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
• Efficient quantification on large-scale networks
• Inductive learning for product assortment graph completion
• Deep Constraint-based Propagation in Graph Neural Networks
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• Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs
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• GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation
• Accelerating the identification of informative reduced representations of proteins with deep learning for graphs
• Network Representation Learning: From Traditional Feature Learning to Deep Learning
• A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring