Results 11 to 20 of about 7,871 (219)
On Manipulating Weight Predictions in Signed Weighted Networks [PDF]
AAAI Conference on Artificial Intelligence, 2023 Adversarial social network analysis studies how graphs can be rewired or otherwise manipulated to evade social network analysis tools. While there is ample literature on manipulating simple networks, more sophisticated network types are much less ...
Tomasz Lizurej +2 more
semanticscholar +1 more source
Learning Weight Signed Network Embedding with Graph Neural Networks
Data Science and Engineering, 2023 AbstractNetwork embedding aims to map nodes in a network to low-dimensional vector representations. Graph neural networks (GNNs) have received much attention and have achieved state-of-the-art performance in learning node representation. Using fundamental sociological theories (status theory and balance theory) to model signed networks, basing GNN on ...
Zekun Lu +4 more
openaire +2 more sources
Polarization and multiscale structural balance in signed networks
Communications Physics, 2023 Polarization, or a division into mutually hostile groups, is a common feature of social systems. It is studied in Structural Balance Theory in terms of semicycles in signed networks.
Szymon Talaga +3 more
doaj +1 more source
Controllability and observability of linear multi-agent systems over matrix-weighted signed networks [PDF]
arXiv.org, 2022 In this paper, the controllability and observability of linear multi-agent systems over matrix-weighted signed networks are analyzed. Firstly, the definition of equitable partition of matrix-weighted signed multi-agent system is given, and the upper ...
Lanhao Zhao +3 more
semanticscholar +1 more source
Online Correlation Clustering for Dynamic Complete Signed Graphs [PDF]
Social Science Research Network, 2022 In the correlation clustering problem for complete signed graphs, the input is a complete signed graph with edges weighted as $+1$ (denote recommendation to put this pair in the same cluster) or $-1$ (recommending to put this pair of vertices in separate
A. Shakiba
semanticscholar +1 more source
Nonlinear Merging Consensus for Multi-Agent Systems on Directed and Weighted Signed Graph [PDF]
IEEE Access, 2020 This paper settles the nonlinear merging consensus for multi-agent systems on a directed and weighted signed network. A novel nonlinear merging control protocol is proposed to drive the states of all agents to arrive at the same state. To be consistent with the reality, the interactions among agents can be either cooperative or competitive and the ...
Shasha Feng +3 more
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WSGMB: weight signed graph neural network for microbial biomarker identification
Briefings in Bioinformatics, 2023 Abstract The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered ...
Shuheng Pan, Xinyi Jiang, Kai Zhang
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Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures [PDF]
Neural Information Processing Systems, 2023 Persistent homology (PH) provides topological descriptors for geometric data, such as weighted graphs, which are interpretable, stable to perturbations, and invariant under, e.g., relabeling.
David Loiseaux +4 more
semanticscholar +1 more source
Signed distance Laplacian matrices for signed graphs [PDF]
Linear and multilinear algebra, 2020 A signed graph is a graph whose edges are labelled either positive or negative. Corresponding to the two signed distance matrices defined for signed graphs, we define two signed distance Laplacian matrices.
Roshni T. Roy +3 more
semanticscholar +1 more source
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation [PDF]
Computer Vision and Pattern Recognition, 2019 We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes.
Alberto Bailoni +6 more
semanticscholar +1 more source