Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network

TL;DR

This paper introduces RITR, a novel unsupervised graph imputation network that effectively handles graphs with both attribute-incomplete and attribute-missing data, outperforming existing methods.

Contribution

The paper presents the first unsupervised framework specifically designed for hybrid-absent graphs with both attribute-incomplete and attribute-missing data.

Findings

RITR outperforms state-of-the-art methods on four datasets.

The initializing-then-refining strategy improves imputation accuracy.

The method effectively handles complex hybrid data absence scenarios.

Abstract

With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer from being absent due to privacy-protecting policies or copyright restrictions during data collection. The absence of graph data can be roughly categorized into attribute-incomplete and attribute-missing circumstances. Specifically, attribute-incomplete indicates that a part of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that the whole attribute vectors of partial nodes are missing. Although many efforts have been devoted, none of them is custom-designed for a common situation where both types of graph data absence exist simultaneously. To fill this gap, we develop a novel network termed Revisiting Initializing Then Refining (RITR), where we complete both attribute-incomplete and attribute-missing samples under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structural matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on four datasets have verified that our methods consistently outperform existing state-of-the-art competitors.