HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption
Self-supervised auto-encoders have emerged as a successful framework for representation learning in computer vision and natural language processing in recent years, However, their application to graph data has been met with limited performance due to the non-Euclidean and complex structure of graphs in comparison to images or text, as well as the limitations of conventional auto-encoder architectures. In this paper, we investigate factors impacting the performance of auto-encoders on graph data and propose a novel auto-encoder model for graph representation learning. Our model incorporates a hierarchical adaptive masking mechanism to incrementally increase the difficulty of training in order to mimic the process of human cognitive learning, and a trainable corruption scheme to enhance the robustness of learned representations. Through extensive experimentation on ten benchmark datasets, we demonstrate the superiority of our proposed method over state-of-the-art graph representation learning models.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Representation LearningRepresentation LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Hi-GMAE: Hierarchical Graph Masked Autoencoders
Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node-level information, categorizing them as…
Graph Neural NetworkSelf-Supervised LearningSeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised learning tasks only and very few work has sh…
Contrastive LearningSelf-Supervised LearningVariational InferenceHierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection
Graph self-supervised learning has gained significant attention recently. However, many existing approaches heavily depend on perturbations, and inappropriate perturbations may corrupt the graph's inherent information. T…
Link PredictionNode ClassificationQuantizationSelf-Supervised LearningGraphMAE: Self-Supervised Masked Graph Autoencoders
Self-supervised learning (SSL) has been extensively explored in recent years. Particularly, generative SSL has seen emerging success in natural language processing and other AI fields, such as the wide adoption of BERT a…
Contrastive LearningGraph ClassificationGraph LearningNode Classification+2Self-Supervised Variational Auto-Encoders
Density estimation, compression and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of g…
Data CompressionDensity EstimationEdge Detection