Self-supervised representation learning: Introduction, advances, and challenges
Self-supervised representation learning (SSRL) methods aim to provide powerful, deep
feature learning without the requirement of large annotated data sets, thus alleviating the …
feature learning without the requirement of large annotated data sets, thus alleviating the …
[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
[HTML][HTML] Pre-trained models: Past, present and future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …
great success and become a milestone in the field of artificial intelligence (AI). Owing to …
Self-supervised learning: Generative or contrastive
Deep supervised learning has achieved great success in the last decade. However, its
defects of heavy dependence on manual labels and vulnerability to attacks have driven …
defects of heavy dependence on manual labels and vulnerability to attacks have driven …
Gcc: Graph contrastive coding for graph neural network pre-training
Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …
world problems. Various downstream graph learning tasks have benefited from its recent …
Heterogeneous graph transformer
Recent years have witnessed the emerging success of graph neural networks (GNNs) for
modeling structured data. However, most GNNs are designed for homogeneous graphs, in …
modeling structured data. However, most GNNs are designed for homogeneous graphs, in …
Gpt-gnn: Generative pre-training of graph neural networks
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-
structured data. However, training GNNs requires abundant task-specific labeled data …
structured data. However, training GNNs requires abundant task-specific labeled data …
A comprehensive survey on automatic knowledge graph construction
Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …
knowledge. To this end, much effort has historically been spent extracting informative fact …
Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism
Graph representation learning has attracted increasing research attention. However, most
existing studies fuse all structural features and node attributes to provide an overarching …
existing studies fuse all structural features and node attributes to provide an overarching …
Oag-bench: a human-curated benchmark for academic graph mining
With the rapid proliferation of scientific literature, versatile academic knowledge services
increasingly rely on comprehensive academic graph mining. Despite the availability of …
increasingly rely on comprehensive academic graph mining. Despite the availability of …