Book ; Online: ${\rm N{\small ode}S{\small ig}}$
Random Walk Diffusion meets Hashing for Scalable Graph Embeddings
2020
Abstract: ... N{\small ode}S{\small ig}}$, a scalable embedding model that computes binary node representations ... rm N{\small ode}S{\small ig}}$ exploits random walk diffusion probabilities via stable random ...
| Abstract | Learning node representations is a crucial task with a plethora of interdisciplinary applications. Nevertheless, as the size of the networks increases, most widely used models face computational challenges to scale to large networks. While there is a recent effort towards designing algorithms that solely deal with scalability issues, most of them behave poorly in terms of accuracy on downstream tasks. In this paper, we aim at studying models that balance the trade-off between efficiency and accuracy. In particular, we propose ${\rm N{\small ode}S{\small ig}}$, a scalable embedding model that computes binary node representations. ${\rm N{\small ode}S{\small ig}}$ exploits random walk diffusion probabilities via stable random projection hashing, towards efficiently computing embeddings in the Hamming space. Our extensive experimental evaluation on various graphs has demonstrated that the proposed model achieves a good balance between accuracy and efficiency compared to well-known baseline models on two downstream tasks. |
|---|---|
| Keywords | Computer Science - Machine Learning ; Computer Science - Social and Information Networks ; Statistics - Machine Learning |
| Subject code | 006 |
| Publishing date | 2020-10-01 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
Full text online
More links
Kategorien
Inter-library loan at ZB MED
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.