Philip A Knight
Biographic Data
| ID | 3925813 |
|---|---|
| NAME | Philip A Knight |
| GIVEN NAMES | Philip A |
| FAMILY NAME | Knight |
| SIGNATURE | KNIGHT P A |
| AFFILIATIONS | University of Strathclyde |
| ORCID | 0000-0001-9511-5692 |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 1 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 1 |
Learning network embeddings using small graphlets
Techniques for learning vectorial representations of graphs (graph embeddings) have recently emerged as an effective approach to facilitate machine learning on graphs. Some of the most popular methods involve sophisticated features such as graph kernels or convolutional networks. In this work, we introduce two straightforward supervised learning algorithms based on small-size graphlet counts, combined with a dimension reduction step. The first re…
Learning network embeddings using small graphlets
Techniques for learning vectorial representations of graphs (graph embeddings) have recently emerged as an effective approach to facilitate machine learning on graphs. Some of the most popular methods involve sophisticated features such as graph kernels or convolutional networks. In this work, we introduce two straightforward supervised learning algorithms based on small-size graphlet counts, combined with a dimension reduction step. The first re…
Learning network embeddings using small graphlets
Techniques for learning vectorial representations of graphs (graph embeddings) have recently emerged as an effective approach to facilitate machine learning on graphs. Some of the most popular methods involve sophisticated features such as graph kernels or convolutional networks. In this work, we introduce two straightforward supervised learning algorithms based on small-size graphlet counts, combined with a dimension reduction step. The first re…
Advanced Graph Neural Networks (1 works) · Artificial Intelligence (1 works) · Bioinformatics and Genomic Networks (1 works) · Complex Network Analysis Techniques (1 works) · Computer Science (1 works) · Dimensionality reduction (1 works) · Feature learning (1 works) · Feature selection (1 works) · Graph (1 works) · Machine learning (1 works)