PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Type
ArticleAuthors
Gui, ShupengZhang, Xiangliang

Zhong, Pan
Qiu, Shuang
Wu, Mingrui
Ye, Jieping
Wang, Zhengdao
Liu, Ji
KAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2021Permanent link to this record
http://hdl.handle.net/10754/660645
Metadata
Show full item recordAbstract
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.2021.3061162Publisher
IEEEarXiv
1909.12903Additional Links
https://ieeexplore.ieee.org/document/9361263/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9361263
ae974a485f413a2113503eed53cd6c53
10.1109/TPAMI.2021.3061162