# Attributed Network Embedding via Subspace Discovery

arXiv: https://arxiv.org/abs/1901.04095 · HTML: https://mlsanity.com/paper/1901.04095/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2019.

## Built on by (most cited first, 11 of 11)

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- [Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networks](https://mlsanity.com/paper/2312.02387/) (2023)
- [Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification](https://mlsanity.com/paper/2405.16672/) (2024)
- [Graph-Based Alternatives to LLMs for Human Simulation](https://mlsanity.com/paper/2511.02135/) (2025)
- [Citation network applications in a scientific co-authorship recommender system](https://mlsanity.com/paper/2111.15466/) (2021)
- [Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network](https://mlsanity.com/paper/2202.06307/) (2022)
- [Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes](https://mlsanity.com/paper/2302.04383/) (2023)
- [Human Action Co-occurrence in Lifestyle Vlogs using Graph Link Prediction](https://mlsanity.com/paper/2309.06219/) (2023)
- [Learning Graph Node Embeddings by Smooth Pair Sampling](https://mlsanity.com/paper/2501.12884/) (2025)

## Abstract (arXiv)

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/daokunzhang/attri2vec.