Modeling customer shopping intentions is a crucial task for e-commerce, ...
Graph neural networks have shown great ability in representation (GNNs)
...
Recommendation systems play a vital role in many online platforms, with ...
Graph Neural Networks (GNNs) have shown great power in various domains.
...
Graph Neural Networks (GNNs) have achieved great success in modeling
gra...
Imitation learning has achieved great success in many sequential
decisio...
Graph-structured data are pervasive in the real-world such as social
net...
Graph Neural Networks (GNNs) have achieved promising results in various ...
Uncovering rationales behind predictions of graph neural networks (GNNs)...
Graph serves as a powerful tool for modeling data that has an underlying...
Graph Neural Networks (GNNs) have shown great ability in modeling
graph-...
Link prediction is an important task that has wide applications in vario...
In recent years, graph neural networks (GNNs) have achieved state-of-the...
In this paper, we study the problem of conducting self-supervised learni...
This work studies the problem of learning unbiased algorithms from biase...
Uncovering rationales behind predictions of graph neural networks (GNNs)...
Graph Neural Networks (GNNs) have made rapid developments in the recent
...
Most existing fair classifiers rely on sensitive attributes to achieve
f...
Edges in real-world graphs are typically formed by a variety of factors ...
Graph Neural Networks (GNNs) have shown their great ability in modeling ...
Graph Neural Networks (GNNs) have achieved remarkable performance in mod...
Graph Neural Networks (GNNs), which generalize the deep neural networks ...
Graph Neural Networks (GNNs) have boosted the performance for many
graph...
The recent advanced deep learning techniques have shown the promising re...
Graph Neural Networks (GNNs) have achieved promising results for
semi-su...
The world is increasingly urbanizing and the building industry accounts ...
Though machine learning models are achieving great success, ex-tensive
s...
Graph translation is very promising research direction and has a wide ra...
Node classification is an important research topic in graph learning. Gr...
A software vulnerability could be exploited without any visible symptoms...
Time series data is prevalent in a wide variety of real-world applicatio...
Graph neural networks (GNNs) have shown great power in modeling graph
st...
In recent years, the proliferation of so-called "fake news" has caused m...
In recent years, Graph Convolutional Networks (GCNs) show competitive
pe...
The success of deep learning notoriously requires larger amounts of cost...
With the rapid growth and prevalence of social network applications (App...
Graph Neural Networks (GNNs) are powerful tools in representation learni...
Nowadays, Internet is a primary source of attaining health information.
...
Multivariate time series (MTS) forecasting is widely used in various dom...
Even though the topic of explainable AI/ML is very popular in text and
c...
Towards the challenging problem of semi-supervised node classification, ...
Real-world graph applications, such as advertisements and product
recomm...
Graph neural networks (GNNs) are widely used in many applications. Howev...
Online users generate tremendous amounts of textual information by
parti...
Graph Neural Networks (GNNs) have boosted the performance of many graph
...
Consuming news from social media is becoming increasingly popular. Socia...
Graph neural networks, which generalize deep neural network models to gr...
In this paper, we develop a novel approach for semi-supervised VAE witho...
Most social media platforms are largely based on text, and users often w...
Social media for news consumption is a double-edged sword. On the one ha...