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    PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions

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    Type
    Article
    Authors
    Gui, Shupeng
    Zhang, Xiangliang cc
    Zhong, Pan
    Qiu, Shuang
    Wu, Mingrui
    Ye, Jieping
    Wang, Zhengdao
    Liu, Ji
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2021
    Permanent link to this record
    http://hdl.handle.net/10754/660645
    
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    Abstract
    Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors Existing approaches specify (either explicitly or implicitly) certain dependencies on neighbors, which may lead to loss of subtle but important structural information within the graph and other dependence among neighbors. This intrigues us to ask the question: can we design a model to give the adaptive flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding method (named PINE) via a novel notion of partial permutation invariant set function, to capture any possible dependence. Our method 1) can learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, and 2) is applicable to both homogeneous and heterogeneous graph embedding, the latter of which is challenged by the diversity of node types. Furthermore, we provide theoretical guarantee for the representation capability of our method for general homogeneous and heterogeneous graphs.
    Citation
    Gui, S., Zhang, X., Zhong, P., Qiu, S., Wu, M., Ye, J., … Liu, J. (2021). PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. doi:10.1109/tpami.2021.3061162
    Publisher
    IEEE
    Journal
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    DOI
    10.1109/TPAMI.2021.3061162
    arXiv
    1909.12903
    Additional Links
    https://ieeexplore.ieee.org/document/9361263/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9361263
    ae974a485f413a2113503eed53cd6c53
    10.1109/TPAMI.2021.3061162
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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