A survey on embedding dynamic graphs
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 …
analytics and inference, supporting applications like node classification, link prediction, and …
Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …
graph-structured data. However, many real-world systems are dynamic in nature, since the …
Self-supervised temporal graph learning with temporal and structural intensity alignment
Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …
tasks with dynamic information, which has recently garnered increasing attention. In contrast …
Temporal network embedding for link prediction via VAE joint attention mechanism
Network representation learning or embedding aims to project the network into a low-
dimensional space that can be devoted to different network tasks. Temporal networks are an …
dimensional space that can be devoted to different network tasks. Temporal networks are an …
Graphgt: Machine learning datasets for graph generation and transformation
Graph generation has shown great potential in applications like network design and mobility
synthesis and is one of the fastest-growing domains in machine learning for graphs. Despite …
synthesis and is one of the fastest-growing domains in machine learning for graphs. Despite …
Embedding temporal networks inductively via mining neighborhood and community influences
Network embedding aims to generate an embedding for each node in a network, which
facilitates downstream machine learning tasks such as node classification and link …
facilitates downstream machine learning tasks such as node classification and link …
Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatiotemporal Forecasting
Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot
in spatiotemporal forecasting problems. While many dynamic graph construction methods …
in spatiotemporal forecasting problems. While many dynamic graph construction methods …
Grass: Learning spatial–temporal properties from chainlike cascade data for microscopic diffusion prediction
H Li, C Xia, T Wang, Z Wang, P Cui… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Information diffusion prediction captures diffusion dynamics of online messages in social
networks. Thus, it is the basis of many essential tasks such as popularity prediction and viral …
networks. Thus, it is the basis of many essential tasks such as popularity prediction and viral …
HB-DSBM: Modeling the dynamic complex networks from community level to node level
A variety of methods have been proposed for modeling and mining dynamic complex
networks, in which the topological structure varies with time. As the most popular and …
networks, in which the topological structure varies with time. As the most popular and …
Holistic Prediction on a Time-Evolving Attributed Graph
Graph-based prediction is essential in NLP tasks such as temporal knowledge graph
completion. A cardinal question in this field is, how to predict the future links, nodes, and …
completion. A cardinal question in this field is, how to predict the future links, nodes, and …