Recommender systems based on graph embedding techniques: A review
Y Deng - IEEE Access, 2022 - ieeexplore.ieee.org
As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …
predict user's preferred items from millions of candidates by analyzing observed user-item …
Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach
Dynamic demand prediction is crucial for the efficient operation and management of urban
transportation systems. Extensive research has been conducted on single-mode demand …
transportation systems. Extensive research has been conducted on single-mode demand …
SybilFlyover: Heterogeneous graph-based fake account detection model on social networks
S Li, J Yang, G Liang, T Li, K Zhao - Knowledge-Based Systems, 2022 - Elsevier
Organized social robot accounts can launch Sybil attacks on online social networks (OSNs)
for various malicious purposes, thus significantly affecting the user experience of online …
for various malicious purposes, thus significantly affecting the user experience of online …
Openhgnn: an open source toolkit for heterogeneous graph neural network
Heterogeneous Graph Neural Networks (HGNNs), as a kind of powerful graph
representation learning methods on heterogeneous graphs, have attracted increasing …
representation learning methods on heterogeneous graphs, have attracted increasing …
Shine: Subhypergraph inductive neural network
Y Luo - Advances in Neural Information Processing …, 2022 - proceedings.neurips.cc
Hypergraph neural networks can model multi-way connections among nodes of the graphs,
which are common in real-world applications such as genetic medicine. In particular, genetic …
which are common in real-world applications such as genetic medicine. In particular, genetic …
Neural graph databases
Graph databases (GDBs) enable processing and analysis of unstructured, complex, rich,
and usually vast graph datasets. Despite the large significance of GDBs in both academia …
and usually vast graph datasets. Despite the large significance of GDBs in both academia …
Advanced Persistent Threat intelligent profiling technique: A survey
BH Tang, JF Wang, Z Yu, B Chen, W Ge, J Yu… - Computers and Electrical …, 2022 - Elsevier
With the boom in Internet and information technology, cyber-attacks are becoming more
frequent and sophisticated, especially Advanced Persistent Threat (APT) attacks. Unlike …
frequent and sophisticated, especially Advanced Persistent Threat (APT) attacks. Unlike …
An inductive graph neural network model for compound–protein interaction prediction based on a homogeneous graph
X Wan, X Wu, D Wang, X Tan, X Liu, Z Fu… - Briefings in …, 2022 - academic.oup.com
Identifying the potential compound–protein interactions (CPIs) plays an essential role in
drug development. The computational approaches for CPI prediction can reduce time and …
drug development. The computational approaches for CPI prediction can reduce time and …
Heterogeneous dynamical academic network for learning scientific impact propagation
Quantifying and predicting the long-term impact of both scientific papers and individual
authors have important implications for many academic policy decisions, from identifying …
authors have important implications for many academic policy decisions, from identifying …
Heterogeneous graph neural network with multi-view representation learning
In recent years, graph neural networks (GNNs)-based methods have been widely adopted
for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich …
for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich …