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    Decentralized Embedding Framework for Large-Scale Networks

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    Name:
    Imran2020_Chapter_DecentralizedEmbeddingFramewor (1).pdf
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    2.002Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Imran, Mubashir
    Yin, Hongzhi
    Chen, Tong
    Shao, Yingxia
    Zhang, Xiangliang cc
    Zhou, Xiaofang
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-09-22
    Online Publication Date
    2020-09-22
    Print Publication Date
    2020
    Embargo End Date
    2021-09-21
    Permanent link to this record
    http://hdl.handle.net/10754/665603
    
    Metadata
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    Abstract
    Network embedding aims to learn vector representations of vertices, that preserve both network structures and properties. However, most existing embedding methods fail to scale to large networks. A few frameworks have been proposed by extending existing methods to cope with network embedding on large-scale networks. These frameworks update the global parameters iteratively or compress the network while learning vector representation. Such network embedding schemes inevitably lead to a high cost of either high communication overhead or sub-optimal embedding quality. In this paper, we propose a novel decentralized large-scale network embedding framework called DeLNE. As the name suggests, DeLNE divides a network into smaller partitions and learn vector representation in a distributed fashion, avoiding any unnecessary communication overhead. Our proposed framework uses Variational Graph Convolution Auto-Encoders to embed the structure and properties of each sub-network. Secondly, we propose an embedding aggregation mechanism, that captures the global properties of each node. Thirdly, we propose an alignment function, that reconciles all sub-networks embedding into the same vector space. Due to the parallel nature of DeLNE, it scales well on large clustered environments. Through extensive experimentation on realistic datasets, we show that DeLNE produces high-quality embedding and outperforms existing large-scale network embeddings frameworks, in terms of both efficiency and effectiveness.
    Citation
    Imran, M., Yin, H., Chen, T., Shao, Y., Zhang, X., & Zhou, X. (2020). Decentralized Embedding Framework for Large-Scale Networks. Lecture Notes in Computer Science, 425–441. doi:10.1007/978-3-030-59419-0_26
    Sponsors
    This work is supported by Australian Research Council (Grant No. DP190101985, DP170103954) and National Natural Science Foundation of China (Grant No. U1936104 and 61702015).
    Publisher
    Springer Nature
    Conference/Event name
    25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
    ISBN
    9783030594183
    DOI
    10.1007/978-3-030-59419-0_26
    Additional Links
    http://link.springer.com/10.1007/978-3-030-59419-0_26
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-59419-0_26
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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