# Incomplete Graph Representation and Learning via Partial Graph Neural Networks

arXiv: https://arxiv.org/abs/2003.10130 · HTML: https://mlsanity.com/paper/2003.10130/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features](https://mlsanity.com/paper/2111.12128/) (2021)
- [Confidence-Based Feature Imputation for Graphs with Partially Known Features](https://mlsanity.com/paper/2305.16618/) (2023)
- [Handling Missing Data via Max-Entropy Regularized Graph Autoencoder](https://mlsanity.com/paper/2211.16771/) (2022)
- [EGG-GAE: scalable graph neural networks for tabular data imputation](https://mlsanity.com/paper/2210.10446/) (2022)
- [GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets](https://mlsanity.com/paper/2306.03447/) (2023)
- [Uncertainty-Aware Robust Learning on Noisy Graphs](https://mlsanity.com/paper/2306.08210/) (2023)
- [Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features](https://mlsanity.com/paper/2302.08250/) (2023)
- [Incomplete Graph Learning: A Comprehensive Survey](https://mlsanity.com/paper/2502.12412/) (2025)
- [Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation](https://mlsanity.com/paper/2507.13368/) (2025)
- [Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective](https://mlsanity.com/paper/2401.11783/) (2024)
- [Remote Work Optimization with Robust Multi-channel Graph Neural Networks](https://mlsanity.com/paper/2209.03150/) (2022)
- [Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphs](https://mlsanity.com/paper/2507.10595/) (2025)
- [On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals](https://mlsanity.com/paper/2509.10874/) (2025)
- [Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity](https://mlsanity.com/paper/2510.11347/) (2025)
- [Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation](https://mlsanity.com/paper/2512.06356/) (2025)
- [Cyberswarm: a novel swarm intelligence algorithm inspired by cyber community dynamics](https://mlsanity.com/paper/2512.14752/) (2025)

## Abstract (arXiv)

Graph Neural Networks (GNNs) are gaining increasing attention on graph data learning tasks in recent years. However, in many applications, graph may be coming in an incomplete form where attributes of graph nodes are partially unknown/missing. Existing GNNs are generally designed on complete graphs which can not deal with attribute-incomplete graph data directly. To address this problem, we develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs), for attribute-incomplete graph representation and learning. Our work is motivated by the observation that the neighborhood aggregation function in standard GNNs can be equivalently viewed as the neighborhood reconstruction formulation. Based on it, we define two novel partial aggregation (reconstruction) functions on incomplete graph and derive PaGNNs for incomplete graph data learning. Extensive experiments on several datasets demonstrate the effectiveness and efficiency of the proposed PaGNNs.