Opinion Dynamics with Multi-body Interactions
@inproceedings{Neuhuser2020OpinionDW, title={Opinion Dynamics with Multi-body Interactions}, author={Leonie Neuh{\"a}user and Michael T. Schaub and Andrew Mellor and Renaud Lambiotte}, booktitle={NetGCooP}, year={2020} }
We introduce and analyse a three-body consensus model (3CM) for non-linear consensus dynamics on hypergraphs. Our model incorporates reinforcing group effects, which can cause shifts in the average state of the system even in if the underlying graph is complete (corresponding to a mean-field interaction), a phenomena that may be interpreted as a type of peer pressure. We further demonstrate that for systems with two clustered groups, already a small asymmetry in our dynamics can lead to the…
10 Citations
Consensus Dynamics and Opinion Formation on Hypergraphs
- Computer ScienceArXiv
- 2021
A unified presentation of the results presented in these publications is provided, which derives and analyse models for consensus dynamics on hypergraphs, and reuses some of the text and results presented there.
Consensus dynamics on temporal hypergraphs
- MathematicsPhysical review. E
- 2021
In addition to an effect on the convergence speed, the final consensus value of the temporal system can differ strongly from the consensus on the aggregated, static hypergraph, and a nonlinear consensus dynamics model in the temporal setting is considered.
Kinetic equations for processes on co-evolving networks
- Computer ScienceKinetic & Related Models
- 2022
This paper derives macroscopic equations for processes on large co-evolving networks, examples being opinion polarization with the emergence of filter bubbles or other social processes such as norm development, and proposes a suitable closure at the level of a two-particle distribution.
Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes
- Computer ScienceArXiv
- 2022
A simplicial threshold model (STM) for nonlinear cascades over simplicial complexes in which dyadic, triadic and higher-order interactions are represented by k-dimensional simplices (i.e., k-simplices) involving (k − 1) vertices/nodes is developed.
What are higher-order networks?
- Computer ScienceArXiv
- 2021
The goals of this survey article are to clarify what higher-order networks are, why these are interesting objects of study, and how they can be used in applications.
Higher-order motif analysis in hypergraphs
- MathematicsCommunications Physics
- 2022
This paper presents a meta-analyses of the immune system’s response to infectious disease and shows clear patterns in response to antibiotics and in particular the immune systems ofType A andType B infection.
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
- Computer ScienceSignal Process.
- 2021
Message passing all the way up
- Computer Science
- 2022
It is shown that any function of interest the authors want to compute over graphs can, in all likelihood, be expressed using pairwise message passing – just over a potentially modified graph, and argued how most practical implementations subtly do this kind of trick anyway.
Principled Simplicial Neural Networks for Trajectory Prediction
- Computer Science, MathematicsICML
- 2021
A simple convolutional architecture is proposed, rooted in tools from algebraic topology, for the problem of trajectory prediction, and it is shown that it obeys all three of these properties when an odd, nonlinear activation function is used.
References
SHOWING 1-10 OF 19 REFERENCES
Social contagion models on hypergraphs
- MathematicsPhysical Review Research
- 2020
An analytical framework is developed and the concept of latent heat is extended to social contexts, which might help understanding oscillatory social behaviors and unfold the research line of higher-order models and the analytical treatment of hypergraphs.
Simplicial models of social contagion
- BiologyNature Communications
- 2019
Higher-order social interactions, the effects of groups, are included in their model of social contagion, enabling insight into why critical masses are required to initiate social changes and contributing to the understanding of higher-order interactions in complex systems.
Dynamics of non-conservative voters
- Mathematics
- 2008
We study a family of opinion formation models in one dimension where the propensity for a voter to align with its local environment depends non-linearly on the fraction of disagreeing neighbors.…
A simple model of global cascades on random networks
- Computer ScienceProceedings of the National Academy of Sciences of the United States of America
- 2002
It is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but anincreasingly heterogeneous degree distribution makes it less vulnerable.
A tutorial on modeling and analysis of dynamic social networks. Part I
- Computer ScienceAnnu. Rev. Control.
- 2017
A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part II
- Computer ScienceAnnu. Rev. Control.
- 2018
Time scale modeling for consensus in sparse directed networks with time-varying topologies
- Mathematics2016 IEEE 55th Conference on Decision and Control (CDC)
- 2016
The paper finds suitable time-varying weights to compute the aggregate variables as time-invariant weighted averages of the states in each cluster to deal with the more challenging time-Varying directed graph case.
Mixing beliefs among interacting agents
- EconomicsAdv. Complex Syst.
- 2000
We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High…
Reaching a Consensus
- Mathematics
- 1974
Abstract Consider a group of individuals who must act together as a team or committee, and suppose that each individual in the group has his own subjective probability distribution for the unknown…
Higher-order organization of complex networks
- Computer ScienceScience
- 2016
A generalized framework for clustering networks on the basis of higher-order connectivity patterns provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges.