[PDF][PDF] Deep graph structure learning for robust representations: A survey
Abstract Graph Neural Networks (GNNs) are widely used for analyzing graph-structured
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …
Homophily-enhanced structure learning for graph clustering
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing
graph neural networks (GNNs) have shown impressive results. Despite the success of …
graph neural networks (GNNs) have shown impressive results. Despite the success of …
Accelerating human–computer interaction through convergent conditions for LLM explanation
The article addresses the accelerating human–machine interaction using the large
language model (LLM). It goes beyond the traditional logical paradigms of explainable …
language model (LLM). It goes beyond the traditional logical paradigms of explainable …
Graph neural networks intersect probabilistic graphical models: A survey
Graphs are a powerful data structure to represent relational data and are widely used to
describe complex real-world data structures. Probabilistic Graphical Models (PGMs) have …
describe complex real-world data structures. Probabilistic Graphical Models (PGMs) have …
Towards Graph Prompt Learning: A Survey and Beyond
Large-scale" pre-train and prompt learning" paradigms have demonstrated remarkable
adaptability, enabling broad applications across diverse domains such as question …
adaptability, enabling broad applications across diverse domains such as question …
PIXEL: Prompt-based Zero-shot Hashing via Visual and Textual Semantic Alignment
Zero-Shot Hashing (ZSH) has aroused significant attention due to its efficiency and
generalizability in multi-modal retrieval scenarios, which aims to encode semantic …
generalizability in multi-modal retrieval scenarios, which aims to encode semantic …
Graph Structure Learning with Bi-level Optimization
N Yin - arxiv preprint arxiv:2411.17062, 2024 - arxiv.org
Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph
structure, improve the robustness of GNN merely from a local view by considering the local …
structure, improve the robustness of GNN merely from a local view by considering the local …
Generic structure extraction with bi-level optimization for graph structure learning
Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph
structure, improve the robustness of GNN merely from a local view by considering the local …
structure, improve the robustness of GNN merely from a local view by considering the local …
Knowledge-based and data-driven underground pressure forecasting based on graph structure learning
Y Wang, M Liu, Y Huang, H Zhou, X Wang… - International Journal of …, 2024 - Springer
The pressure prediction technology whereby represents the rock pressure law in the
excavation is fundamental to safety in production and industrial intelligentization. A growing …
excavation is fundamental to safety in production and industrial intelligentization. A growing …
Robust Airport Surface Object Detection Based on Graph Neural Network
W Tang, H Li - Applied Sciences, 2024 - mdpi.com
Accurate and robust object detection is of critical importance in airport surface surveillance
to ensure the security of air transportation systems. Owing to the constraints imposed by a …
to ensure the security of air transportation systems. Owing to the constraints imposed by a …