Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

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Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) ◽

10.1109/spawc48557.2020.9154336 ◽

2020 ◽

Author(s):

Navid Naderializadeh ◽

Mark Eisen ◽

Alejandro Ribeiro

Keyword(s):

Neural Networks ◽

Power Control ◽

Wireless Power ◽

Graph Neural Networks

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Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks

Molecules ◽

10.3390/molecules26206185 ◽

2021 ◽

Vol 26 (20) ◽

pp. 6185

Author(s):

Oliver Wieder ◽

Mélaine Kuenemann ◽

Marcus Wieder ◽

Thomas Seidel ◽

Christophe Meyer ◽

...

Keyword(s):

Neural Networks ◽

Performance Metrics ◽

Graph Isomorphism ◽

Aqueous Solubility ◽

Solubility Prediction ◽

Robust Solution ◽

Composite Graph ◽

Edge Graph ◽

Graph Neural Networks ◽

Key Aspects

The accurate prediction of molecular properties, such as lipophilicity and aqueous solubility, are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods, such as graph-based neural networks (GNNs), have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN). It is composed of two distinct sub-architectures (D-MPNN, GIN) and achieves an improvement in accuracy over its sub-architectures employing various learning, and featurization strategies. We argue that combining models with different key aspects help make graph neural networks deeper and simultaneously increase their predictive power. Furthermore, we address current limitations in assessment of deep-learning models, namely, comparison of single training run performance metrics, and offer a more robust solution.

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Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2020.2988255 ◽

2020 ◽

Vol 68 ◽

pp. 2977-2991 ◽

Cited By ~ 2

Author(s):

Mark Eisen ◽

Alejandro Ribeiro

Keyword(s):

Neural Networks ◽

Resource Allocation ◽

Edge Graph ◽

Graph Neural Networks ◽

Wireless Resource Allocation

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Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5942 ◽

2020 ◽

Vol 34 (04) ◽

pp. 5021-5028 ◽

Cited By ~ 2

Author(s):

Yadan Luo ◽

Zi Huang ◽

Zheng Zhang ◽

Ziwei Wang ◽

Mahsa Baktashmotlagh ◽

...

Keyword(s):

Neural Networks ◽

Message Passing ◽

Shot Classification ◽

The Past ◽

Meta Learning ◽

Error Accumulation ◽

Effectiveness And Efficiency ◽

Graph Neural Networks ◽

Continual Learning

Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data. However, most mainstream models suffer from catastrophic forgetting and insufficient robustness issues, thereby failing to fully retain or exploit long-term knowledge while being prone to cause severe error accumulation. In this paper, we propose a novel Continual Meta-Learning approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically formulates meta-learning as continual learning of a sequence of tasks. With each task forming as a graph, the intra- and inter-task correlations can be well preserved via message-passing and history transition. To remedy topological uncertainty from graph initialization, we utilize Bayes by Backprop strategy that approximates the posterior distribution of task-specific parameters with amortized inference networks, which are seamlessly integrated into the end-to-end edge learning. Extensive experiments conducted on the miniImageNet and tieredImageNet datasets demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 42.8% compared with state-of-the-art on the miniImageNet 5-way 1-shot classification task.

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Graph Neural Networks for Prediction of Fuel Ignition Quality

10.26434/chemrxiv.12280325.v1 ◽

2020 ◽

Author(s):

Artur Schweidtmann ◽

Jan Rittig ◽

Andrea König ◽

Martin Grohe ◽

Alexander Mitsos ◽

...

Keyword(s):

Neural Networks ◽

Octane Number ◽

Molecular Graph ◽

Chemical Properties ◽

Graph Representation ◽

Structure Property ◽

Oxygenated Hydrocarbons ◽

Physico Chemical ◽

Ignition Quality ◽

Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

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