In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Facial expression cognition technology continues to face challenges from certain
perspectives despite the fact that there have been significant recent learning advances in …

Abstract The recently proposed Graph Convolutional Networks (GCNs) have achieved
significantly superior performance on various graph-related tasks, such as node …

GraphSAGE is a widely-used graph neural network for classification, which generates node
embeddings in two steps: sampling and aggregation. In this paper, we introduce causal …

Measuring the similarity between legal documents to find prior documents from a massive
collection that are similar to a current document is an essential component in legal assistant …

Graph data, also known as complex network data, is omnipresent across various domains
and applications. Prior graph neural network models primarily focused on extracting task …

The complexity and non-Euclidean structure of graph data hinders the development of data
augmentation methods similar to those in computer vision. In this paper, we propose a …

In recent years, Graph Neural Networks (GNNs) have gained popularity for solving a wide
range of problems, primarily due to the proliferation of graph data across various domains …

The complexity and non-Euclidean structure of graph data hinder the development of data
augmentation methods similar to those in computer vision. In this paper, we propose a …

Graph neural networks (GNNs), which work with graph-structured data, have attracted
considerable attention and achieved promising performance on graph-related tasks. While …