Modeling the Role of Relationship Fading and Breakup in Social Network Formation.
Ontology highlight
ABSTRACT:
SUBMITTER: Murase Y
PROVIDER: S-EPMC4503738 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
Modeling the Role of Relationship Fading and Breakup in Social Network Formation.
Murase Yohsuke Y Jo Hang-Hyun HH Török János J Kertész János J Kaski Kimmo K
PloS one 20150715 7
In social networks of human individuals, social relationships do not necessarily last forever as they can either fade gradually with time, resulting in "link aging," or terminate abruptly, causing "link deletion," as even old friendships may cease. In this paper, we study a social network formation model where we introduce several ways by which a link termination takes place. If we adopt the link aging, we get a more modular structure with more homogeneously distributed link weights within commu ...[more]
Similar Datasets
Project description:Nearly half of the freshwater flux from the Antarctic Ice Sheet into the Southern Ocean occurs in the form of large tabular icebergs that calve off the continent's ice shelves. However, because of difficulties in adequately simulating their breakup, large Antarctic icebergs to date have either not been represented in models or represented but with no breakup scheme such that they consistently survive too long and travel too far compared with observations. Here, we introduce a representation of iceberg fracturing using a breakup scheme based on the "footloose mechanism." We optimize the parameters of this breakup scheme by forcing the iceberg model with an ocean state estimate and comparing the modeled iceberg trajectories and areas with the Antarctic Iceberg Tracking Database. We show that including large icebergs and a representation of their breakup substantially affects the iceberg meltwater distribution, with implications for the circulation and stratification of the Southern Ocean.
| S-EPMC7744088 | biostudies-literature
Project description:Experiencing stress can have a disadvantageous effect on mental well-being. Additional to the relation between suffering from chronic stress and depression, both stress (acute and chronic) and depression are associated with cognitive alterations, including working memory. The breakup of a relationship is considered to be a stressful event that can lead to symptoms of depression in otherwise healthy people. Additional to elevated depression scores, stress-related cognitive alterations may occur in this population as well. Therefore, in the present fMRI study we investigated whether experiencing a relationship breakup is associated with working memory alterations and whether this is related to depressive symptom severity. A three workload version of the n-back task (0-back, 1-back, 2-back) was used to measure working memory in subjects who experienced a breakup in the preceding 6 months ("heartbreak group", n = 70) and subjects in a romantic relationship ("relationship group", n = 46). Behavioral task performance was compared between the two groups. Functional MRI scans were analyzed using General Linear Model (GLM) activation analyses. Workload conditions were contrasted to each other and to baseline and group differences were assessed. To investigate whether brain networks are associated with depressive symptom severity within the heartbreak group specifically, a post hoc feature-based Independent Component Analysis was performed on the 2-back > 0-back contrast images to identify brain regions that covaried across subjects. Behaviorally, the heartbreak group performed similar at high workload (i.e., 2-back) and better at moderate workload (i.e., 1-back) than the relationship group. GLM analysis revealed an interaction between group and 2-back > 0-back, 2-back > 1-back and 2-back > baseline; the heartbreak group showed less precuneus activation compared to the relationship group. Furthermore, within the heartbreak group, we found a negative association between depressive symptom severity and a brain network representing mostly the precuneus, anterior cingulate gyrus and supplementary motor cortex. Our findings suggest that the effect of a breakup is accompanied by workload-dependent working memory alterations. Therefore, we propose that this population can potentially be used to investigate the interplay between stress, cognitive functioning and depression.
| S-EPMC8062740 | biostudies-literature
Project description:The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect.
| S-EPMC9095566 | biostudies-literature
Project description:Predicting different forms of collective behavior in human populations, as the outcome of individual attitudes and their mutual influence, is a question of major interest in social sciences. In particular, processes of opinion formation have been theoretically modeled on the basis of a formal similarity with the dynamics of certain physical systems, giving rise to an extensive collection of mathematical models amenable to numerical simulation or even to exact solution. Empirical ground for these models is however largely missing, which confine them to the level of mere metaphors of the real phenomena they aim at explaining. In this paper we present results of an experiment which quantifies the change in the opinions given by a subject on a set of specific matters under the influence of others. The setup is a variant of a recently proposed experiment, where the subject's confidence on his or her opinion was evaluated as well. In our realization, which records the quantitative answers of 85 subjects to 20 questions before and after an influence event, the focus is put on characterizing the change in answers and confidence induced by such influence. Similarities and differences with the previous version of the experiment are highlighted. We find that confidence changes are to a large extent independent of any other recorded quantity, while opinion changes are strongly modulated by the original confidence. On the other hand, opinion changes are not influenced by the initial difference with the reference opinion. The typical time scales on which opinion varies are moreover substantially longer than those of confidence change. Experimental results are then used to estimate parameters for a dynamical agent-based model of opinion formation in a large population. In the context of the model, we study the convergence to full consensus and the effect of opinion leaders on the collective distribution of opinions.
| S-EPMC4627778 | biostudies-literature
Project description:Advanced cancers, such as prostate and breast cancers, commonly metastasize to bone. In the bone matrix, dendritic osteocytes form a spatial network allowing communication between osteocytes and the osteoblasts located on the bone surface. This communication network facilitates coordinated bone remodeling. In the presence of a cancerous microenvironment, the topology of this network changes. In those situations, osteocytes often appear to be either overdifferentiated (i.e., there are more dendrites than healthy bone) or underdeveloped (i.e., dendrites do not fully form). In addition to structural changes, histological sections from metastatic breast cancer xenografted mice show that number of osteocytes per unit area is different between healthy bone and cancerous bone. We present a stochastic agent-based model for bone formation incorporating osteoblasts and osteocytes that allows us to probe both network structure and density of osteocytes in bone. Our model both allows for the simulation of our spatial network model and analysis of mean-field equations in the form of integro-partial differential equations. We considered variations of our model to study specific physiological hypotheses related to osteoblast differentiation; for example predicting how changing biological parameters, such as rates of bone secretion, rates of cancer formation, and rates of osteoblast differentiation can allow for qualitatively different network topologies. We then used our model to explore how commonly applied therapies such as bisphosphonates (e.g., zoledronic acid) impact osteocyte network formation.
| S-EPMC7387425 | biostudies-literature
Project description:Terahertz (THz) band offers a vast amount of bandwidth and is envisioned to become a key enabler for a number of next generation wireless applications. In this direction, appropriate channel models, encapsulating the large and small-scale fading phenomena, need to be developed for both indoor and outdoor communications environments. The THz large-scale fading characteristics have been extensively investigated for both indoor and outdoor scenarios. The study of indoor THz small-scale fading has recently gained the momentum, while the small-scale fading of outdoor THz wireless channels has not yet been investigated. Motivated by this, this contribution introduces Gaussian mixture (GM) distribution as a suitable small-scale fading model for outdoor THz wireless links. In more detail, multiple outdoor THz wireless measurements recorded at different transceiver separation distance are fed to an expectation-maximization fitting algorithm, which returns the parameters of the GM probability density function. The fitting accuracy of the analytical GMs is evaluated in terms of the Kolmogorov-Smirnov, Kullback-Leibler (KL) and root-mean-square-error (RMSE) tests. The results reveal that as the number of mixtures increases the resulting analytical GMs perform a better fit to the empirical distributions. In addition, the KL and RMSE metrics indicate that the increase of mixtures beyond a particular number result to no significant improvement of the fitting accuracy. Finally, following the same approach as in the case of GM, we examine the suitability of mixture Gamma to capture the small-scale fading characteristics of the outdoor THz channels.
| S-EPMC10115853 | biostudies-literature
Project description:Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers-Logistic Regression, Naïve Bayes and Random Forest-with a range of social network measures and the necessary databases to model the verdicts in two real-world cases: the U.S. Watergate Conspiracy of the 1970's and the now-defunct Canada-based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.
| S-EPMC4732755 | biostudies-literature
Project description:Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.
| S-EPMC4450406 | biostudies-literature
Project description:Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.
| S-EPMC4731493 | biostudies-literature
Project description:Breakups are common among emerging adults and are associated with elevated depressive and anxiety symptoms, especially in the presence of attachment insecurities. Previous authors have suggested that inadequate coping strategies might explain this association, yet this has not been examined longitudinally. This study examined the mediating role of five coping strategies (self-help, approach, accommodation, avoidance, self-punishment) in the longitudinal associations between attachment insecurities (anxiety, avoidance) and depressive and anxious symptoms in 196 emerging adults experiencing a romantic breakup. Measures of pre-breakup attachment, post-breakup coping strategies (one-month post-breakup), and depressive and anxiety symptoms (one- and three-month post-breakup) were administered. Results from a longitudinal autoregressive cross-lagged model showed that pre-breakup attachment insecurities were related to higher depressive and anxiety post-breakup symptoms through higher use of self-punishment and lower use of accommodation coping strategies. Findings highlight coping strategies as potential intervention targets to promote the recovery of emerging adults experiencing breakup distress.
| S-EPMC10727987 | biostudies-literature