With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine
learning tasks. For node-level tasks, GNNs have strong power to model the homophily …

Graph Neural Networks (GNNs) have shown superior performance in modeling graph data.
Existing studies have shown that a lot of GNNs perform well on homophilic graphs while …

Recently, deep learning methods have become mainstream in code search since they do
better at capturing semantic correlations between code snippets and search queries and …

Graph Neural Networks (GNNs) have shown expressive performance on graph
representation learning by aggregating information from neighbors. Recently, some studies …

Network Embedding aims to learn a function mapping the nodes to Euclidean space
contribute to multiple learning analysis tasks on networks. However, both the noisy …

User engagement prediction plays a critical role in designing interaction strategies to grow
user engagement and increase revenue in online social platforms. Through the in-depth …

Initialization plays a critical role in the training of deep neural networks (DNN). Existing
initialization strategies mainly focus on stabilizing the training process to mitigate gradient …

Collaborative Filtering (CF), a classical recommender system approach, learns users'
interests and behavioral preferences for items through a user–item interaction graph. CF …

Despite the recent success of Graph Neural Networks (GNNs), their learning pipeline is
guided only by the input graph and the desired output of certain tasks, failing to capture …