A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
A survey of graph neural networks for social recommender systems
Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …
interactions as well as the user-to-user social relations for the task of generating item …
Walklm: A uniform language model fine-tuning framework for attributed graph embedding
Graphs are widely used to model interconnected entities and improve downstream
predictions in various real-world applications. However, real-world graphs nowadays are …
predictions in various real-world applications. However, real-world graphs nowadays are …
A survey on graph representation learning methods
S Khoshraftar, A An - ACM Transactions on Intelligent Systems and …, 2024 - dl.acm.org
Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …
goal of graph representation learning is to generate graph representation vectors that …
Parallel and distributed graph neural networks: An in-depth concurrency analysis
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …
routinely solve complex problems on unstructured networks, such as node classification …
[HTML][HTML] Health-aware food recommendation system with dual attention in heterogeneous graphs
Recommender systems (RS) have been increasingly applied to food and health. However,
challenges still remain, including the effective incorporation of heterogeneous information …
challenges still remain, including the effective incorporation of heterogeneous information …
Higpt: Heterogeneous graph language model
Heterogeneous graph learning aims to capture complex relationships and diverse relational
semantics among entities in a heterogeneous graph to obtain meaningful representations …
semantics among entities in a heterogeneous graph to obtain meaningful representations …
Vessel trajectory prediction based on spatio-temporal graph convolutional network for complex and crowded sea areas
S Wang, Y Li, H Xing, Z Zhang - Ocean Engineering, 2024 - Elsevier
In order to improve the navigation ability of vessels and ensure the safety of maritime traffic,
vessel trajectory prediction plays a crucial role in the intelligent navigation and collision …
vessel trajectory prediction plays a crucial role in the intelligent navigation and collision …
Empowering cyberattack identification in IoHT networks with neighborhood component-based improvised long short-term memory
Cybersecurity has become an inevitable concern in the healthcare industry due to the rapid
growth of the Internet of Health Things (IoHT). The IoHT is revolutionizing healthcare by …
growth of the Internet of Health Things (IoHT). The IoHT is revolutionizing healthcare by …
Higher order heterogeneous graph neural network based on node attribute enhancement
Heterogeneous graph neural networks (HGNNs) have garnered significant attention owing
to their ability to capture attribute information from heterogeneous graphs (HGs). However …
to their ability to capture attribute information from heterogeneous graphs (HGs). However …