RA-HGNN: Attribute completion of heterogeneous graph neural networks based on residual attention mechanism
Z Zhao, Z Liu, Y Wang, D Yang, W Che - Expert Systems with Applications, 2023 - Elsevier
Heterogeneous graphs, which are also called heterogeneous information networks, analyze
the different types of nodes in an information network and the different types of links between …
the different types of nodes in an information network and the different types of links between …
Homophily-oriented heterogeneous graph rewiring
With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG)
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …
Revisiting multi-view learning: A perspective of implicitly heterogeneous Graph Convolutional Network
Abstract Graph Convolutional Network (GCN) has become a hotspot in graph-based
machine learning due to its powerful graph processing capability. Most of the existing GCN …
machine learning due to its powerful graph processing capability. Most of the existing GCN …
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks
This work considers the problem of heterogeneous graph-level anomaly detection.
Heterogeneous graphs are commonly used to represent behaviours between different types …
Heterogeneous graphs are commonly used to represent behaviours between different types …
[HTML][HTML] Keyword-enhanced recommender system based on inductive graph matrix completion
Going beyond the user–item rating information, recent studies have utilized additional
information to improve the performance of recommender systems. Graph neural network …
information to improve the performance of recommender systems. Graph neural network …
Node classification oriented Adaptive Multichannel Heterogeneous Graph Neural Network
Y Li, C Jian, G Zang, C Song, X Yuan - Knowledge-Based Systems, 2024 - Elsevier
Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing
node classification on heterogeneous graphs (HGs). These models are built on the …
node classification on heterogeneous graphs (HGs). These models are built on the …
Hierarchical bottleneck for heterogeneous graph representation
Heterogeneous graphs (HGs) contain many nodes and their interaction relationships, which
can model complex systems and provide rich semantic and structural information for task …
can model complex systems and provide rich semantic and structural information for task …
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both
academia and industry because prevailing studies on heterogeneous graphs often suffer …
academia and industry because prevailing studies on heterogeneous graphs often suffer …
An Efficient Subgraph-Inferring Framework for Large-Scale Heterogeneous Graphs
Heterogeneous Graph Neural Networks (HGNNs) play a vital role in advancing the field of
graph representation learning by addressing the complexities arising from diverse data …
graph representation learning by addressing the complexities arising from diverse data …
Joint Spectrum, Precoding, and Phase Shifts Design for RIS-Aided Multiuser MIMO THz Systems
A Mehrabian, VWS Wong - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Terahertz (THz) wireless systems aim to support content-rich applications with ultra-high
data rate. Due to high molecular absorption, THz signals experience severe path loss over …
data rate. Due to high molecular absorption, THz signals experience severe path loss over …