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RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks
The Web Conference, 2023Signed graphs model complex relations using both positive and negative edges. Signed graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the vulnerability of SGNN to potential edge noise in the input graph. Our goal is to
Zeyu Zhang +7 more
semanticscholar +1 more source
Fixed-Time Bipartite Containment Control of High-Order Multi-Agent Systems in the Signed Graph
IEEE Transactions on Circuits and Systems - II - Express Briefs, 2023The fixed-time bipartite containment control for high-order multi-agent systems (MASs) with disturbances is discussed in directed signed graph. The proposed control algorithm guarantees that the trajectories of followers converge into the time-varying ...
Heng Zhan +4 more
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Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics
AAAI Conference on Artificial Intelligence, 2023Modeling continuous-time dynamics constitutes a foundational challenge, and uncovering inter-component correlations within complex systems holds promise for enhancing the efficacy of dynamic modeling.
Lanlan Chen, Kai Wu, Jian Lou, Jing Liu
semanticscholar +1 more source
Signed Graph Balancing with Graph Cut
European Signal Processing Conference, 2023Signed graphs–graphs with both positive and negative edge weights–are useful to specify pairwise dissimilarities as well as similarities in data. However, unlike graph variation operators (e.g., adjacency and graph Laplacian matrices) for unsigned graphs,
Haruki Yokota +3 more
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scSGL: kernelized signed graph learning for single-cell gene regulatory network inference
Bioinform., 2022MOTIVATION Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing ...
Abdullah Karaaslanli +3 more
semanticscholar +1 more source
SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions
J. Comput. Biol., 2022Capturing comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, graph neural networks (GNNs) have received increasing attention in the drug discovery domain due to their ...
Ming Chen +5 more
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Signed Graph Neural Network with Latent Groups
Knowledge Discovery and Data Mining, 2021Signed graph representation learning is an effective approach to analyze the complex patterns in real-world signed graphs with the co-existence of positive and negative links.
Haoxin Liu +6 more
semanticscholar +1 more source
International Journal of Control, 2021
This paper examines the bipartite tracking consensus for nonlinear multi-agents over a directed signed graph under bounded disturbances and a non-zero bounded input of the leader.
Amina Shams, M. Rehan, M. Tufail
semanticscholar +1 more source
This paper examines the bipartite tracking consensus for nonlinear multi-agents over a directed signed graph under bounded disturbances and a non-zero bounded input of the leader.
Amina Shams, M. Rehan, M. Tufail
semanticscholar +1 more source
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations
Briefings Bioinform., 2021MiRNAs are a class of small non-coding RNA molecules that play an important role in many biological processes, and determining miRNA-disease associations can benefit drug development and clinical diagnosis.
Guangzhan Zhang +5 more
semanticscholar +1 more source