Distinguishing mechanisms of social contagion from local network view

• URL: http://arxiv.org/abs/2406.18519v2
• Date: Thu, 27 Jun 2024 12:34:37 GMT
• Title: Distinguishing mechanisms of social contagion from local network view
• Authors: Elsa Andres, Gergely Ódor, Iacopo Iacopini, Márton Karsai,
• Abstract summary: Multiple adoption rules may coexist even within the same social contagion process. Our goal is to understand whether coexisting adoption mechanisms can be distinguished from a microscopic view. This study offers a novel perspective on the observations of propagation processes at the egocentric level.
• Score: 0.02499907423888048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The adoption of individual behavioural patterns is largely determined by stimuli arriving from peers via social interactions or from external sources. Based on these influences, individuals are commonly assumed to follow simple or complex adoption rules, inducing social contagion processes. In reality, multiple adoption rules may coexist even within the same social contagion process, introducing additional complexity into the spreading phenomena. Our goal is to understand whether coexisting adoption mechanisms can be distinguished from a microscopic view, at the egocentric network level, without requiring global information about the underlying network, or the unfolding spreading process. We formulate this question as a classification problem, and study it through a Bayesian likelihood approach and with random forest classifiers in various synthetic and data-driven experiments. This study offers a novel perspective on the observations of propagation processes at the egocentric level and a better understanding of landmark contagion mechanisms from a local view.

Related papers

• Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
  We propose leveraging the principle of chunking to interpret artificial neural population activities. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities. We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
  arXiv  Detail & Related papers  (2025-02-03T20:30:46Z)
• Covariate Dependent Mixture of Bayesian Networks [3.493206543979227]
  Using a single network structure for inference can be misleading, as it may not capture sub-population differences. We propose a novel approach of modelling a mixture of Bayesian networks where component probabilities depend on individual characteristics. Our method identifies both network structures and demographic predictors of sub-population membership, aiding personalised interventions.
  arXiv  Detail & Related papers  (2025-01-10T06:21:48Z)
• Emergence of human-like polarization among large language model agents [61.622596148368906]
  We simulate a networked system involving thousands of large language model agents, discovering their social interactions, result in human-like polarization. Similarities between humans and LLM agents raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate it.
  arXiv  Detail & Related papers  (2025-01-09T11:45:05Z)
• Causal Representation Learning from Multimodal Biological Observations [57.00712157758845]
  We aim to develop flexible identification conditions for multimodal data. We establish identifiability guarantees for each latent component, extending the subspace identification results from prior work. Our key theoretical ingredient is the structural sparsity of the causal connections among distinct modalities.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. We employ a neural network trained to identify causality through supervised learning on simulated data. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Navigating the swarm: Deep neural networks command emergent behaviours [2.7059353835118602]
  We show that it is possible to generate coordinated structures in collective behavior with intended global patterns by fine-tuning an inter-agent interaction rule. Our strategy employs deep neural networks, obeying the laws of dynamics, to find interaction rules that command desired structures. Our findings pave the way for new applications in robotic swarm operations, active matter organisation, and for the uncovering of obscure interaction rules in biological systems.
  arXiv  Detail & Related papers  (2024-07-16T02:46:11Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts [1.1059590443280727]
  We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We derive estimates of the underlying latent state and associated patterns of neural activity.
  arXiv  Detail & Related papers  (2023-04-18T15:15:00Z)
• Predicting the long-term collective behaviour of fish pairs with deep learning [52.83927369492564]
  This study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach to experiments and to the results of a state-of-the-art analytical model. We demonstrate that machine learning models social interactions can directly compete with their analytical counterparts in subtle experimental observables.
  arXiv  Detail & Related papers  (2023-02-14T05:25:03Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning [11.247894240593691]
  We build systems that feed each sensory input from the environment into distinct, but identical neural networks. We show that these sensory networks can be trained to integrate information received locally, and through communication via an attention mechanism, can collectively produce a globally coherent policy.
  arXiv  Detail & Related papers  (2021-09-07T05:12:50Z)
• Empirically Classifying Network Mechanisms [0.0]
  Network models are used to study interconnected systems across many physical, biological, and social disciplines. We introduce a simple empirical approach which can mechanistically classify arbitrary network data.
  arXiv  Detail & Related papers  (2020-12-22T01:41:34Z)
• Visualizing and Understanding Vision System [0.6510507449705342]
  We use a vision recognition-reconstruction network (RRN) to investigate the development, recognition, learning and forgetting mechanisms. In digit recognition study, we witness that the RRN could maintain object invariance representation under various viewing conditions. In the learning and forgetting study, novel structure recognition is implemented by adjusting entire synapses in low magnitude while pattern specificities of original synaptic connectivity are preserved.
  arXiv  Detail & Related papers  (2020-06-11T07:08:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.