A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
A new adapter tuning of large language model for chinese medical named entity recognition
L Zhou, Y Chen, X Li, Y Li, N Li, X Wang… - Applied Artificial …, 2024 - Taylor & Francis
Named entity recognition (NER) is a crucial step in extracting medical information from
Chinese text, and fine-tuning large language models (LLMs) for this task is an effective …
Chinese text, and fine-tuning large language models (LLMs) for this task is an effective …
Demystifying Higher-Order Graph Neural Networks
Higher-order graph neural networks (HOGNNs) are an important class of GNN models that
harness polyadic relations between vertices beyond plain edges. They have been used to …
harness polyadic relations between vertices beyond plain edges. They have been used to …
Communication Hierarchy-aware Graph Engine for Distributed Model Training
Efficient processing of large-scale graphs with billions to trillions of edges is essential for
training graph-based large language models (LLMs) in web-scale systems. The increasing …
training graph-based large language models (LLMs) in web-scale systems. The increasing …