Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/1908.05387 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909047764025344 |
|---|---|
| author | Saebi, Mandana Ciampaglia, Giovanni Luca Kaplan, Lance M Chawla, Nitesh V |
| author_facet | Saebi, Mandana Ciampaglia, Giovanni Luca Kaplan, Lance M Chawla, Nitesh V |
| contents | Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher-order dependencies in the network. Thus, the embeddings that are generated may not accurately represent of the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this paper presents HONEM, a higher-order network embedding method that captures the non-Markovian higher-order dependencies in a network. HONEM is specifically designed for the higher-order network structure (HON) and outperforms other state-of-the-art methods in node classification, network re-construction, link prediction, and visualization for networks that contain non-Markovian higher-order dependencies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1908_05387 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | HONEM: Learning Embedding for Higher Order Networks Saebi, Mandana Ciampaglia, Giovanni Luca Kaplan, Lance M Chawla, Nitesh V Machine Learning Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher-order dependencies in the network. Thus, the embeddings that are generated may not accurately represent of the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this paper presents HONEM, a higher-order network embedding method that captures the non-Markovian higher-order dependencies in a network. HONEM is specifically designed for the higher-order network structure (HON) and outperforms other state-of-the-art methods in node classification, network re-construction, link prediction, and visualization for networks that contain non-Markovian higher-order dependencies. |
| title | HONEM: Learning Embedding for Higher Order Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1908.05387 |