# BiasedWalk: Biased Sampling for Representation Learning on Graphs

arXiv: https://arxiv.org/abs/1809.02482 · HTML: https://mlsanity.com/paper/1809.02482/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations](https://mlsanity.com/paper/2204.05885/) (2022)
- [Multiple Kernel Representation Learning on Networks](https://mlsanity.com/paper/2106.05057/) (2021)
- [STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2406.04035/) (2024)
- [Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood](https://mlsanity.com/paper/2104.05234/) (2021)
- [Topic-aware latent models for representation learning on networks](https://mlsanity.com/paper/2111.05576/) (2021)

## Abstract (arXiv)

Network embedding algorithms are able to learn latent feature representations of nodes, transforming networks into lower dimensional vector representations. Typical key applications, which have effectively been addressed using network embeddings, include link prediction, multilabel classification and community detection. In this paper, we propose BiasedWalk, a scalable, unsupervised feature learning algorithm that is based on biased random walks to sample context information about each node in the network. Our random-walk based sampling can behave as Breath-First-Search (BFS) and Depth-First-Search (DFS) samplings with the goal to capture homophily and role equivalence between the nodes in the network. We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks. The experiment results show that the proposed method outperforms the baseline ones in most of the tasks and datasets.