# Multi-Label Graph Convolutional Network Representation Learning

arXiv: https://arxiv.org/abs/1912.11757 · HTML: https://mlsanity.com/paper/1912.11757/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [Multi-label Node Classification On Graph-Structured Data](https://mlsanity.com/paper/2304.10398/) (2023)
- [RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training](https://mlsanity.com/paper/2110.07336/) (2021)
- [Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features](https://mlsanity.com/paper/2208.09562/) (2022)
- [GraSSNet: Graph Soft Sensing Neural Networks](https://mlsanity.com/paper/2111.06980/) (2021)
- [ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network](https://mlsanity.com/paper/2312.05736/) (2023)
- [Network Embedding via Deep Prediction Model](https://mlsanity.com/paper/2104.13323/) (2021)
- [Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning](https://mlsanity.com/paper/2301.01143/) (2023)
- [Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study](https://mlsanity.com/paper/2412.17961/) (2024)
- [Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2608.25653/) (2026)
- [Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution](https://mlsanity.com/paper/2608.27574/) (2026)

## Abstract (arXiv)

Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels, e.g., a user may belong to diverse interest groups of a social network, resulting in multi-label networks for many applications. The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning. In this paper, we propose a novel multi-label graph convolutional network (ML-GCN) for learning node representation for multi-label networks. To fully explore label-label correlation and network topology structures, we propose to model a multi-label network as two Siamese GCNs: a node-node-label graph and a label-label-node graph. The two GCNs each handle one aspect of representation learning for nodes and labels, respectively, and they are seamlessly integrated under one objective function. The learned label representations can effectively preserve the inner-label interaction and node label properties, and are then aggregated to enhance the node representation learning under a unified training framework. Experiments and comparisons on multi-label node classification validate the effectiveness of our proposed approach.