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

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 2; pp. 770 - 782
• Main Authors: Gui, Shupeng, Zhang, Xiangliang, Zhong, Pan, Qiu, Shuang, Wu, Mingrui, Ye, Jieping, Wang, Zhengdao, Liu, Ji
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregates; Cognitive tasks; Embedding; Games; Graph embedding; Graph neural networks; Graphs; Invariants; Laplace equations; Machine learning; Matrix decomposition; Nodes; partial permutation invariant set function; Permutations; Reinforcement learning; representation learning; Representations; Task analysis
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2021.3061162

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 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. 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.