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    DDHH: A decentralized deep learning framework for large-scale heterogeneous networks

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    Type
    Conference Paper
    Authors
    Imran, Mubashir
    Yin, Hongzhi
    Chen, Tong
    Huang, Zi
    Zhang, Xiangliang cc
    Zheng, Kai
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2021-04
    Permanent link to this record
    http://hdl.handle.net/10754/670753
    
    Metadata
    Show full item record
    Abstract
    Learning vector representations (i.e., embeddings) of nodes for graph-structured information network has attracted vast interest from both industry and academia. Most real-world networks exhibit a complex and heterogeneous format, enclosing high-order relationships and rich semantic information among nodes. However, existing heterogeneous network embedding (HNE) frameworks are commonly designed in a centralized fashion, i.e., all the data storage and learning process take place on a single machine. Hence, those HNE methods show severe performance bottlenecks when handling large-scale networks due to high consumption on memory, storage, and running time. In light of this, to cope with large-scale HNE tasks with strong efficiency and effectiveness guarantee, we propose Decentralized Deep Heterogeneous Hypergraph (DDHH) embedding framework in this paper. In DDHH, we innovatively formulate a large heterogeneous network as a hypergraph, where its hyperedges can connect a set of semantically similar nodes. Our framework then intelligently partitions the heterogeneous network using the identified hyperedges. Then, each resulted subnetwork is assigned to a distributed worker, which employs the deep information maximization theorem to locally learn node embeddings from the partition received. We further devise a novel embedding alignment scheme to precisely project independently learned node embeddings from all subnetworks onto a public vector space, thus allowing for downstream tasks. As shown from our experimental results, DDHH significantly improves the efficiency and accuracy of existing HNE models, and can easily scale up to large-scale heterogeneous networks.
    Citation
    Imran, M., Yin, H., Chen, T., Huang, Z., Zhang, X., & Zheng, K. (2021). DDHH: A Decentralized Deep Learning Framework for Large-scale Heterogeneous Networks. 2021 IEEE 37th International Conference on Data Engineering (ICDE). doi:10.1109/icde51399.2021.00196
    Sponsors
    This work was supported by ARC Discovery Project (GrantNo.DP190101985, DP170103954).
    Publisher
    IEEE
    Conference/Event name
    37th IEEE International Conference on Data Engineering, ICDE 2021
    ISBN
    9781728191843
    DOI
    10.1109/ICDE51399.2021.00196
    Additional Links
    https://ieeexplore.ieee.org/document/9458648/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDE51399.2021.00196
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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