The role of link redundancy and structural heterogeneity in network disintegration
While link redundancy has long been acknowledged as a critical factor in network
robustness, current approaches frequently neglect the inherent heterogeneity of structure …
robustness, current approaches frequently neglect the inherent heterogeneity of structure …
Quantifying discriminability of evaluation metrics in link prediction for real networks
Link prediction is one of the most productive branches in network science, aiming to predict
links that would have existed but have not yet been observed, or links that will appear during …
links that would have existed but have not yet been observed, or links that will appear during …
Spatial network disintegration based on ranking aggregation
Disintegrating harmful networks presents a significant challenge, especially in spatial
networks where both topological and geospatial features must be considered. Existing …
networks where both topological and geospatial features must be considered. Existing …
Centrality-based and similarity-based neighborhood extension in graph neural networks
M Zohrabi, S Saravani… - The Journal of …, 2024 - Springer
Abstract In recent years, Graph Neural Networks (GNNs) have become a key technique to
address various graph-based machine learning tasks. Most of existing GNNs use simple …
address various graph-based machine learning tasks. Most of existing GNNs use simple …
Multilingual entity alignment by abductive knowledge reasoning on multiple knowledge graphs
Objectives: Entity alignment (EA) seeks to identify similar real-world objects in different
multilingual knowledge graphs (KGs), also known as ontology alignment. EA assists in …
multilingual knowledge graphs (KGs), also known as ontology alignment. EA assists in …
An epidemic spread model with nonlinear recovery rates on meta-population networks
J Chen, Y Zhang, Y Xu, C Xia, J Tanimoto - Nonlinear Dynamics, 2024 - Springer
During the COVID-19 pandemic, many countries face healthcare system collapses, then
leading governments to recognize the importance of rational allocation of limited medical …
leading governments to recognize the importance of rational allocation of limited medical …
[HTML][HTML] Network Dismantling on Signed Network by Evolutionary Deep Reinforcement Learning
Y Ou, F Xiong, H Zhang, H Li - Sensors, 2024 - mdpi.com
Network dismantling is an important question that has attracted much attention from many
different research areas, including the disruption of criminal organizations, the maintenance …
different research areas, including the disruption of criminal organizations, the maintenance …
ESND: An Embedding-based Framework for Signed Network Dismantling
Network dismantling aims to maximize the disintegration of a network by removing a specific
set of nodes or edges and is applied to various tasks in diverse domains, such as cracking …
set of nodes or edges and is applied to various tasks in diverse domains, such as cracking …
Dynamics behavior of a novel infectious disease model considering population mobility on complex network
Y Qin, L Yang, Z Gu - International Journal of Dynamics and Control, 2024 - Springer
To describe the impact of population mobility between different cities on the spread of
infectious disease, a new infectious disease complex dynamical model is proposed …
infectious disease, a new infectious disease complex dynamical model is proposed …
Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network
C Yu, M Li, W Zongning, K Gao… - … Science and Technology, 2024 - iopscience.iop.org
Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible
to compound faults, causal relationships are hidden within the signal features. Complex …
to compound faults, causal relationships are hidden within the signal features. Complex …