Graph representation learning is an effective tool for facilitating graph analysis with machine
learning methods. Most GNNs, including Graph Convolutional Networks (GCN), Graph …

In the process of numerically modeling natural languages, developing language
embeddings is a vital step. However, it is challenging to develop functional embeddings for …

A real-world graph has a complex topological structure, which is often formed by the
interaction of different latent factors. Disentanglement of these latent factors can effectively …

Abstract Pre-trained Language Models (PLMs) have shown remarkable success in general-
domain text tasks, but their application in the clinical domain is constrained by specialized …

Abstract Pre-trained Language Models (PLMs) have been one of the fundamental
components of natural language processing techniques over the past few years, and have …

For real-world graph data, the complex relationship between nodes is often represented as
a hard binary link. Obviously, it is a discrete and simplified form of continuous relationship …