Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests
from both industrial and academic communities. With its rapid development, the …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

Many tasks in graph machine learning, such as link prediction and node classification, are
typically solved using representation learning. Each node or edge in the network is encoded …

Representation learning based on dynamic graphs has received a lot of attention in recent
years due to its wide range of application scenarios. Although many discrete or continuous …

Deep learning models, such as convolutional neural networks, have long been applied to
image and multi-media tasks, particularly those with structured data. More recently, there …

Autoencoders gained popularity in the deep learning revolution given their ability to
compress data and provide dimensionality reduction. Although prominent deep learning …

Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …

Temporal link prediction on dynamic graphs is essential to various areas such as
recommendation systems, social networks, and citation analysis, and thus attracts great …

Origin-destination (OD) crowd flow, if more accurately inferred at a fine-grained level, has
the potential to enhance the efficacy of various urban applications. While in practice for …