Designing Laplacian flows for opinion clustering in structurally balanced and unbalanced networks
In this work, we consider a group of n agents whose interactions can be represented using unsigned or signed structurally balanced graphs or a special case of structurally unbalanced graphs. A Laplacian-based model is proposed to govern the evolution of opinions. The objective of the paper is to analyze the proposed opinion model on the opinion evolution of the agents. Further, we also determine the conditions required to apply the proposed Laplacian-based opinion model. Finally, some numerical results are shown to validate these results.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
External Bias and Opinion Clustering in Cooperative Networks
In this work, we consider a group of n agents which interact with each other in a cooperative framework. A Laplacian-based model is proposed to govern the evolution of opinions in the group when the agents are subjected …
ClusteringDominant Groups and Asymmetric Polarization in Generalized Quasi-Structurally Balanced Networks
The paper focuses on the phenomenon of asymmetric polarization arising in the presence of a dominant group in the network. The existing works in the literature analyze polarization primarily in structurally and quasi-str…
Network Distance Based on Laplacian Flows on Graphs
Distance plays a fundamental role in measuring similarity between objects. Various visualization techniques and learning tasks in statistics and machine learning such as shape matching, classification, dimension reductio…
ClusteringDimensionality ReductionKoopman-based spectral clustering of directed and time-evolving graphs
While spectral clustering algorithms for undirected graphs are well established and have been successfully applied to unsupervised machine learning problems ranging from image segmentation and genome sequencing to signal…
ClusteringImage SegmentationSemantic SegmentationLocal Graph Clustering with Network Lasso
We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundary and s…
ClusteringGraph Clustering