The Experts below are selected from a list of 200826 Experts worldwide ranked by ideXlab platform

Ji Liu - One of the best experts on this subject based on the ideXlab platform.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Shupeng Gui, Xiangliang Zhang, Pan Zhong, Zhengdao Wang, Shuang Qiu, Ji Liu
    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.

Shupeng Gui - One of the best experts on this subject based on the ideXlab platform.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Shupeng Gui, Xiangliang Zhang, Pan Zhong, Zhengdao Wang, Shuang Qiu, Ji Liu
    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.

Zhengdao Wang - One of the best experts on this subject based on the ideXlab platform.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Shupeng Gui, Xiangliang Zhang, Pan Zhong, Zhengdao Wang, Shuang Qiu, Ji Liu
    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.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    arXiv: Learning, 2019
    Co-Authors: Xiangliang Zhang, Pan Zhong, Mingrui Wu, Jieping Ye, Zhengdao Wang
    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 dependencies among neighbors. This intrigues us to ask the question: can we design a model to give the maximal flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding (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, withour 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. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.

Xiangliang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Shupeng Gui, Xiangliang Zhang, Pan Zhong, Zhengdao Wang, Shuang Qiu, Ji Liu
    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.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    arXiv: Learning, 2019
    Co-Authors: Xiangliang Zhang, Pan Zhong, Mingrui Wu, Jieping Ye, Zhengdao Wang
    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 dependencies among neighbors. This intrigues us to ask the question: can we design a model to give the maximal flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding (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, withour 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. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.

Pan Zhong - One of the best experts on this subject based on the ideXlab platform.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Shupeng Gui, Xiangliang Zhang, Pan Zhong, Zhengdao Wang, Shuang Qiu, Ji Liu
    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.

  • pine universal deep embedding for graph nodes via partial permutation invariant set Functions
    arXiv: Learning, 2019
    Co-Authors: Xiangliang Zhang, Pan Zhong, Mingrui Wu, Jieping Ye, Zhengdao Wang
    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 dependencies among neighbors. This intrigues us to ask the question: can we design a model to give the maximal flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding (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, withour 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. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.