With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

Recent advancements in experimental high-throughput technologies have expanded the
availability and quantity of molecular data in biology. Given the importance of interactions in …

Graph neural networks (GNNs) have been widely used in representation learning on graphs
and achieved state-of-the-art performance in tasks such as node classification and link …

Graph Neural Networks (GNNs) are an effective framework for representation learning of
graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …

Machine learning on graphs is an important and ubiquitous task with applications ranging
from drug design to friendship recommendation in social networks. The primary challenge in …

Network embedding (or graph embedding) has been widely used in many real-world
applications. However, existing methods mainly focus on networks with single-typed …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …

Graph deep learning models, such as graph convolutional networks (GCN) achieve
remarkable performance for tasks on graph data. Similar to other types of deep models …