Graph Deep Learning: State of the Art and Challenges

Georgousis, Stavros; Kenning, Michael P.; Xie, Xianghua

doi:10.1109/access.2021.3055280

Cited by 47 publications

(29 citation statements)

References 86 publications

(153 reference statements)

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“…The popularity of the rapidly growing field of deep learning on GNNs is also reflected by the numerous recent surveys on graph representations and their applications. Existing reviews provide a comprehensive overview of deep learning for non-Euclidean data, graph deep learning frameworks and a taxonomy of existing techniques [ 4 , 10 ] or introduce general applications that cover biology and signal processing domains [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

133

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Introductionmentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

133

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“…S7A) due to the significant time delay between the cell fate events. This indicates that the proper treatment of the temporal axis was necessary, which is why GNN approaches based on static graphs [43, 44] are not directly applicable to the problem of multicellular kinetics. Furthermore, although different models have recently been proposed to deal with evolving graphs with nodes and edges being added or removed [45–47], to the best of our knowledge, there has been no example of a graph-based approach dealing with replication of nodes (i.e., cell division).…”

Section: Discussionmentioning

confidence: 99%

Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks

Yamamoto

Cockburn

Kawaguchi

2021

Preprint

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Extracting the rules of cell-to-cell interactions in tissue dynamics is challenging even if high-resolution live microscopy is accessible. In order to seek and compare the different rules enforced in developing and homeostatic tissues, it will be desirable to have a systematic method that outputs the rules of multi-cellular kinetics simply from live images and cell tracks. Here we demonstrate that graph neural network (GNN)-based models can predict cell fate in the mammalian epidermis when spatiotemporal graph constructed from cell tracks and contacts are given as inputs. By extracting the rules learned by GNN, we find neighbor cell fate inductions and inhibitions consistent with previous findings as well as some that have been previously overlooked. This study demonstrates how GNN-based methods can be useful in inferring stochastic dynamics such as multi-cellular kinetics involving proliferation and loss of agents.

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“…Related works. So far, several surveys have reviewed deep graph-related approaches such as those mainly focusing on graph representation learning methods [6], [72]- [78], graph attention models [79], knowledge graph research [80], [81], attack and defense techniques on graph data [82], and graph matching approaches [83], [84]. Although most of these surveys have made a passing reference to some modern graph generators, this field requires individual attention due to its value and growing popularity.…”

Section: Introductionmentioning

confidence: 99%

Deep Graph Generators: A Survey

et al. 2021

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Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the advances in graph-based deep learning, and in particular graph representation learning, deep graph generation methods have recently emerged with new applications ranging from discovering novel molecular structures to modeling social networks. This paper conducts a comprehensive survey on deep learning-based graph generation approaches and classifies them into five broad categories, namely, autoregressive, autoencoder-based, reinforcement learning-based, adversarial, and flow-based graph generators, providing the readers a detailed description of the methods in each class. We also present publicly available source codes, commonly used datasets, and the most widely utilized evaluation metrics. Finally, we review current trends and suggest future research directions based on the existing challenges.

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Graph Deep Learning: State of the Art and Challenges

Cited by 47 publications

References 86 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks

Deep Graph Generators: A Survey

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