Control-based Graph Embeddings with Data Augmentation for Contrastive Learning

2024-03-07 · Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir, Waseem Abbas, Xenofon Koutsoukos

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.

📄 PDF Abstract BibTeX arXiv:2403.04923

Code (0)

등록된 구현이 없습니다.

Tasks

Contrastive Learning Data Augmentation Graph Representation Learning Representation Learning

Methods 이 논문이 사용한 방법론

Contrastive Learning 설명 없음

LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning

2024-09-02 · Haoran Yang, Xiangyu Zhao, Sirui Huang, Qing Li 외

Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learning on Text-Attributed Graphs (TAGs) has y…

Contrastive Learning Data Augmentation Graph Learning Language Modelling+1

Spectral Augmentations for Graph Contrastive Learning

2023-02-06 · Amur Ghose, Yingxue Zhang, Jianye Hao, Mark Coates

Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. Despite successes, t…

Contrastive Learning Graph Embedding Graph Representation Learning Representation Learning

A Simple Graph Contrastive Learning Framework for Short Text Classification

2025-01-16 · Yonghao Liu, Fausto Giunchiglia, Lan Huang, Ximing Li 외

Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown pro…

Contrastive Learning Data Augmentation Graph Learning text-classification+1

Temporal Graph Representation Learning with Adaptive Augmentation Contrastive

2023-11-07 · Hongjiang Chen, Pengfei Jiao, Huijun Tang, Huaming Wu

Temporal graph representation learning aims to generate low-dimensional dynamic node embeddings to capture temporal information as well as structural and property information. Current representation learning methods for …

Contrastive Learning Graph Representation Learning Representation Learning

TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering

2024-09-27 · Chengkai Liu, Jianling Wang, James Caverlee

In the domain of recommendation and collaborative filtering, Graph Contrastive Learning (GCL) has become an influential approach. Nevertheless, the reasons for the effectiveness of contrastive learning are still not well…

Collaborative Filtering Contrastive Learning Data Augmentation