Inexact Graph Representation Learning

Yijun Duan, Xin Liu, Adam Jatowt, Hai-Tao Yu, Steven J. Lynden, Akiyoshi Matono

Abstract: Graph is the universal language for modeling data across various domains. In recent years, graph representation learning has achieved outstanding performance in a series of computational tasks on graphs, such as graph node classification and community detection, etc. However, a commonly hidden assumption underlying these tasks is that label information corresponds to nodes in the training set on a one-to-one basis. In real-world scenarios, node label information may be uncertain and concealed within higher-order graph structural labels. In this paper, we define a set of arbitrary nodes on the graph as a bag and assume that label information corresponds to bags rather than individual nodes. Furthermore, bag labels are generated based on node-level labels through some hidden mechanism, thus inexactly encompassing node label information1. Therefore, we propose for the first time a novel and widely applicable task: learning the latent representation of bags on the graph and predicting their labels. For this task, we propose a hierarchical model that is highly interpretable and scalable, incorporating information propagation among nodes, information aggregation from nodes to bags and inter-bag relationship prediction. Experiments on diverse standard datasets demonstrate that our proposed model shows higher classification accuracy compared to strong baselines. Our research introduces a new perspective to the field of graph representation learning.