Learning network embeddings using small graphlets
Bibliographic Data
| ID | 4614019 |
|---|---|
| Authors | Luce Le Gorrec (0000-0003-2666-0405, University of Strathclyde, corresponding author), Philip A Knight (0000-0001-9511-5692, University of Strathclyde), Auguste Caen (0000-0002-3338-9206, University of Edinburgh) |
| Year | 2022 |
| Volume | 12 |
| Issue | 1 |
| Publication date | 2022-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-021-00846-9 |
| OpenAlex | W4200281516 |
| Language | EN |
| Citations received | 1 |
| References cited | 45 |
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 relies on a classic feature extraction method powered by principal component analysis (PCA). The second is a feature selection procedure also based on PCA. Despite their conceptual simplicity, these embeddings are arguably more meaningful than some popular alternatives and at the same time are competitive with state-of-the-art methods. We illustrate this second point on a downstream classification task. We then use our algorithms in a novel setting, namely to conduct an analysis of author relationships in Wikipedia articles, for which we present an original dataset. Finally, we provide empirical evidence suggesting that our methods could also be adapted to unsupervised learning algorithms
Dimensionality reduction · Feature learning · Feature selection · Graph · Machine learning · Advanced Graph Neural Networks · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · Computer Science · Artificial Intelligence · Theoretical Computer Science
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |