Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917643148066816 |
|---|---|
| author | Ahmad, Obaid Ullah Said, Anwar Shabbir, Mudassir Abbas, Waseem Koutsoukos, Xenofon |
| author_facet | Ahmad, Obaid Ullah Said, Anwar Shabbir, Mudassir Abbas, Waseem Koutsoukos, Xenofon |
| contents | In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04923 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Control-based Graph Embeddings with Data Augmentation for Contrastive Learning Ahmad, Obaid Ullah Said, Anwar Shabbir, Mudassir Abbas, Waseem Koutsoukos, Xenofon Machine Learning Multiagent Systems Systems and Control In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning. |
| title | Control-based Graph Embeddings with Data Augmentation for Contrastive Learning |
| topic | Machine Learning Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2403.04923 |