A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai, Hongyun; Zheng, Vincent W.; Chang, Kevin Chen-Chuan

doi:10.1109/tkde.2018.2807452

Cited by 1,658 publications

(945 citation statements)

References 87 publications

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“…While being accurate, training a supervised-learning model on the adjacency matrix (SL-A) can take some computational time and resources as the size of the molecular network increases, thus considerably differing in speed for, say, STRING-EXP (14,089 nodes and 141,629 unweighted edges) and GIANT-TN (25,689 nodes and 38,904,929 weighted edges). Worthy of note in this context is the recent excitement in deriving node embeddings for each node in a network, concisely encoding its connectivity to all other nodes, and using them as features in SL algorithms for node classification [60,[69][70][71][72][73][74] . Although we show that SL-A markedly outperforms supervised-learning on the embedding matrix (SL-E; Fig.…”

Section: Discussionmentioning

confidence: 99%

Supervised-learning is an accurate method for network-based gene classification

Liu

Mancuso

Yannakopoulos

et al. 2019

Preprint

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Background: Assigning every human gene to specific functions, diseases, and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods such as supervised-learning and label-propagation that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine learning technique across fields, supervised-learning has been applied only in a few network-based studies for predicting pathway-, phenotype-, or disease-associated genes. It is unknown how supervised-learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label-propagation, the widely-benchmarked canonical approach for this problem. Results:In this study, we present a comprehensive benchmarking of supervised-learning for network-based gene classification, evaluating this approach and a state-of-the-art label-propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised-learning on a gene's full network connectivity outperforms label-propagation and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label-propagation's appeal for naturally using network topology. We further show that supervised-learning on the full network is also superior to learning on node-embeddings (derived using node2vec ), an increasingly popular approach for concisely representing network connectivity. Conclusion:These results show that supervised-learning is an accurate approach for prioritizing genes associated with diverse functions, diseases, and traits and should be considered a staple of network-based gene classification workflows. The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available.

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Section: Discussionmentioning

confidence: 99%

Supervised-learning is an accurate method for network-based gene classification

Liu

Mancuso

Yannakopoulos

et al. 2019

Preprint

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“…In addition, many ecological datasets exhibit graph structure related to phylogenies, social networks, or network-like spatial structure. While it is possible to adapt convolutional neural networks to operate on distance matrices computed from graphs (Fioravanti et al 2018), there are a variety of graph representation learning approaches that provide embeddings for nodes that encode network structure and node attributes (Hamilton et al 2017;Cai et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Neural hierarchical models of ecological populations

Joseph

2019

Preprint

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Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This paper describes an emergent class of hierarchical models parameterized by neural networks -neural hierarchical models -and develops a case study with bird populations in the United States. The derivation of such models is outlined beginning with the relationship between linear regression and neural networks, and analogously between a hierarchical ecological model (a dynamic occupancy model), and its neural counterpart. The resulting neural hierarchical model, trained on millions of detection/non-detection time series for hundreds of species, provides insights into colonization and extinction at a continental scale. The model also demonstrates good quantitative predictive performance and reasonable qualitative results about range shifts and macroecological patterns of population persistence at range boundaries. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modeling that combines the function representation power of neural networks with the inferential capacity of hierarchical models.

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“…Comprehensive surveys of network embedding algorithms can be found elsewhere . There is an immense catalogue of algorithms, and code is distributed in a rushing pace (over 50 network embedding packages are available, many of them released during the last 2 years; https://github.com/chihming/awesome-network-embedding).…”

Section: Towards Biological Embeddingsmentioning

confidence: 99%

“…Comprehensive surveys of network embedding algorithms can be found elsewhere. 107,108,110 There is an immense catalogue of algorithms, and code is distributed in a rushing pace (over 50 network embedding packages are available, many of them released during the last 2 years; https://github.com/chihming/awesome-network-embedding). Families of successful network embedding algorithms include adjacency matrix factorizations (e.g., graph Laplacian eigenmaps), local linear embeddings, isomaps, and a series of deep learning implementations that address several scenarios, such as the case of attributed networks or the preservation of network structure and properties.…”

Section: Figurementioning

confidence: 99%

Formatting biological big data for modern machine learning in drug discovery

Duran‐Frigola

Fernández‐Torras

Bertoni

et al. 2018

WIREs Comput Mol Sci

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Biological data is accumulating at an unprecedented rate, escalating the role of data‐driven methods in computational drug discovery. This scenario is favored by recent advances in machine learning algorithms, which are optimized for huge datasets and consistently beat the predictive performance of previous art, rapidly approaching human expert reasoning. The urge to couple biological data to cutting‐edge machine learning has spurred developments in data integration and knowledge representation, especially in the form of heterogeneous, multiplex and semantically‐rich biological networks. Today, thanks to the propitious rise in knowledge embedding techniques, these large and complex biological networks can be converted to a vector format that suits the majority of machine learning implementations. Here, we explain why this can be particularly transformative for drug discovery where, for decades, customary chemoinformatics methods have employed vector descriptors of compound structures as the standard input of their prediction tasks. A common vector format to represent biology and chemistry may push biological information into most of the existing steps of the drug discovery pipeline, boosting the accuracy of predictions and uncovering connections between small molecules and other biological entities such as targets or diseases. This article is categorized under: Computer and Information Science > Databases and Expert Systems Computer and Information Science > Chemoinformatics

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A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cited by 1,658 publications

References 87 publications

Supervised-learning is an accurate method for network-based gene classification

Supervised-learning is an accurate method for network-based gene classification

Neural hierarchical models of ecological populations

Formatting biological big data for modern machine learning in drug discovery

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