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Network representation learning: a systematic literature review

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Abstract

Omnipresent network/graph data generally have the characteristics of nonlinearity, sparseness, dynamicity and heterogeneity, which bring numerous challenges to network related analysis problem. Recently, influenced by the excellent ability of deep learning to learn representation from data, representation learning for network data has gradually become a new research hotspot. Network representation learning aims to learn a project from given network data in the original topological space to low-dimensional vector space, while encoding a variety of structural and semantic information. The vector representation obtained could effectively support extensive tasks such as node classification, node clustering, link prediction and graph classification. In this survey, we comprehensively present an overview of a large number of network representation learning algorithms from two clear points of view of homogeneous network and heterogeneous network. The corresponding algorithms are deeply analyzed. Extensive applications are introduced in an all-round way, and related experiments are conducted to validate the typical algorithms. Finally, we point out five future promising directions for next research in terms of theory and application.

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Notes

  1. https://github.com/rusty1s/pytorch_geometric.

  2. https://github.com/thunlp/OpenNE.

  3. https://github.com/thunlp/OpenKE.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Number: U1433116), and the Fundamental Research Funds for the Central Universities (Grant Number: NP2017208). Thanks are due to all developers and researchers providing open-source platform. Thanks are due to all the anonymous reviewers.

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    Bentian Li & Dechang Pi

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 211106, China

    Dechang Pi

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Li, B., Pi, D. Network representation learning: a systematic literature review. Neural Comput & Applic 32, 16647–16679 (2020). https://doi.org/10.1007/s00521-020-04908-5

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