Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings
By Bryan Perozzi, Vivek Kulkarni, Haochen Chen, Steven S. Skiena. 2016
204 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Rik Sarkar
- Carl Allen
- Ryan Davies
- Benedek Rozemberczki
- Charles Sutton
- Miltiadis Allamanis
- Rishabh Singh
- Sahil Bhatia
In Computational Theory and Mathematics
In Statistical and Nonlinear Physics
- Rik Sarkar
- Carl Allen
- Ryan Davies
- Benedek Rozemberczki
- Charles Sutton
- Muhan Zhang
- Xiyuan Wang
- Zhewei Wei
Citations in the corpus
- GEMSEC: Graph Embedding with Self Clustering (2018, methodology): “In experimental evaluation we demonstrate that GEMSEC outperforms – in clustering quality – the state of the art neighborhood based [8], [9], multi-scale [16], [17] and community aware embedding methods [13], [14], [15].”
- Multi-scale Attributed Node Embedding (2019, methodology): “Table 2 (top) shows the hyperparameters used for our algorithms, which are consistent other random walk based approaches (Perozzi et al., 2014; Grover & Leskovec, 2016; Ribeiro et al., 2017; Perozzi et al., 2017).”