scholarly journals The robustness of reciprocity: Experimental evidence that each form of reciprocity is robust to the presence of other forms of reciprocity

2020 ◽  
Vol 6 (23) ◽  
pp. eaba0504
Author(s):  
David Melamed ◽  
Brent Simpson ◽  
Jered Abernathy
Keyword(s):  
Social Networks ◽  
Prosocial Behavior ◽  
Experimental Evidence ◽  
Real World ◽  
Strong Support ◽  
Theoretical Models ◽  
Current Understanding ◽  
The Real ◽  
Topological Features

Prosocial behavior is paradoxical because it often entails a cost to one’s own welfare to benefit others. Theoretical models suggest that prosociality is driven by several forms of reciprocity. Although we know a great deal about how each of these forms operates in isolation, they are rarely isolated in the real world. Rather, the topological features of human social networks are such that people are often confronted with multiple types of reciprocity simultaneously. Does our current understanding of human prosociality break down if we account for the fact that the various forms of reciprocity tend to co-occur in nature? Results of a large experiment show that each basis of human reciprocity is remarkably robust to the presence of other bases. This lends strong support to existing models of prosociality and puts theory and research on firmer ground in explaining the high levels of prosociality observed in human social networks.

scholarly journals Modularity based Community Detection in Social Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 564-569
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Research Community ◽  
Incorrect Result ◽  
The Real

The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.

The epidemic network construction and immunization based on node strength

2018 ◽  
Vol 32 (26) ◽  
pp. 1850319 ◽  
Author(s):  
Fuzhong Nian ◽  
Longjing Wang ◽  
Zhongkai Dang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Scale Free Network ◽  
Network Construction ◽  
The Real ◽  
Scale Free ◽  
Social Ability ◽  
The Social ◽  
Free Network

In this paper, a new spreading network was constructed and the corresponding immunizations were proposed. The social ability of individuals in the real human social networks was reflected by the node strength. The negativity and positivity degrees were also introduced. And the edge weights were calculated by the negativity and positivity degrees, respectively. Based on these concepts, a new asymmetric edge weights scale-free network which was more close to the real world was established. The comparing experiments indicate that the proposed immunization is priority to the acquaintance immunization, and close to the target immunization.

The social costs of punishment

2012 ◽  
Vol 35 (1) ◽  
pp. 42-43 ◽  
Author(s):  
Pieter van den Berg ◽  
Lucas Molleman ◽  
Franz J. Weissing
Keyword(s):  
Social Networks ◽  
Real World ◽  
Cooperative Behavior ◽  
Social Costs ◽  
The Real ◽  
Lab Experiments ◽  
The Social ◽  
Direct Payments

AbstractLab experiments on punishment are of limited relevance for understanding cooperative behavior in the real world. In real interactions, punishment is not cheap, but the costs of punishment are of a different nature than in experiments. They do not correspond to direct payments or payoff deductions, but they arise from the repercussions punishment has on social networks and future interactions.

scholarly journals Complexity in Individual Trajectories toward Online Extremism

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Z. Cao ◽  
M. Zheng ◽  
Y. Vorobyeva ◽  
C. Song ◽  
N. F. Johnson
Keyword(s):  
Real World ◽  
Geographical Location ◽  
Strong Support ◽  
Islamic State ◽  
The Real ◽  
Social Media Platforms ◽  
Expansion Theory ◽  
Pass Through ◽  
High Level ◽  
The Impact

Society faces a fundamental global problem of understanding which individuals are currently developing strong support for some extremist entity such as ISIS (Islamic State), even if they never end up doing anything in the real world. The importance of online connectivity in developing intent has been confirmed by recent case studies of already convicted terrorists. Here we use ideas from Complexity to identify dynamical patterns in the online trajectories that individuals take toward developing a high level of extremist support, specifically, for ISIS. Strong memory effects emerge among individuals whose transition is fastest and hence may become “out of the blue” threats in the real world. A generalization of diagrammatic expansion theory helps quantify these characteristics, including the impact of changes in geographical location, and can facilitate prediction of future risks. By quantifying the trajectories that individuals follow on their journey toward expressing high levels of pro-ISIS support—irrespective of whether they then carry out a real-world attack or not—our findings can help move safety debates beyond reliance on static watch-list identifiers such as ethnic background or immigration status and/or postfact interviews with already convicted individuals. Given the broad commonality of social media platforms, our results likely apply quite generally; for example, even on Telegram where (like Twitter) there is no built-in group feature as in our study, individuals tend to collectively build and pass through the so-called super-group accounts.

scholarly journals Reading William Faulkner’s Yoknapatawpha in the COVID-19 era

2020 ◽  
Vol 6 (6) ◽  
Author(s):  
Mourad Romdhani
Keyword(s):  
Social Networks ◽  
Domestic Violence ◽  
William Faulkner ◽  
Real World ◽  
The Real ◽  
Global Pandemic ◽  
Feminist Theories

Caught in the dilemma of the real and the fictitious, one can only wonder about the connection between literature and the Covid 19 global pandemic. As researcher interested in the writings of William Faulkner, I cannot help drawing analogies between the writer’s fictional Yoknapatawpha and our current Covid 19 situation. In the gendered reactions to the pandemic-imposed reality Yoknapatawpha is always resonant. Masculine rejection of face masks and the ideology underlying such a reaction, the mandatory lockdown which consequently led to rising domestic violence in addition to the popular slogan “My body, my choice” which went viral in social networks are all a reiteration of the narrative of Faulkner’s Yoknapatawpha. Drawing analogies between our contemporary real world and Faulkner’s fictitious county will lead to the conclusion that western cultures and societies have reproduced the same patriarchal ideologies and practices that governed Faulkner’s Yoknapatawpha, turning the writer’s narrative world into a universal world that cannot be anchored in place or time. The paper will study the three phenomena as social realities that echo Faulkner’s fictitious county while referring to psychoanalytical and feminist theories.

scholarly journals Majority Vote in Social Networks: Make Random Friends or Be Stubborn to Overpower Elites

2021 ◽  
Author(s):  
Charlotte Out ◽  
Ahad N. Zehmakan
Keyword(s):  
Social Networks ◽  
Real World ◽  
Majority Vote ◽  
Graph Models ◽  
Stabilization Time ◽  
The Real ◽  
Random Graph Models ◽  
Small Set ◽  
High Degree ◽  
Majority Model

Consider a graph G, representing a social network. Assume that initially each node is colored either black or white, which corresponds to a positive or negative opinion regarding a consumer product or a technological innovation. In the majority model, in each round all nodes simultaneously update their color to the most frequent color among their connections. Experiments on the graph data from the real world social networks (SNs) suggest that if all nodes in an extremely small set of high-degree nodes, often referred to as the elites, agree on a color, that color becomes the dominant color at the end of the process. We propose two countermeasures that can be adopted by individual nodes relatively easily and guarantee that the elites will not have this disproportionate power to engineer the dominant output color. The first countermeasure essentially requires each node to make some new connections at random while the second one demands the nodes to be more reluctant towards changing their color (opinion). We verify their effectiveness and correctness both theoretically and experimentally. We also investigate the majority model and a variant of it when the initial coloring is random on the real world SNs and several random graph models. In particular, our results on the Erdős-Rényi, and regular random graphs confirm or support several theoretical findings or conjectures by the prior work regarding the threshold behavior of the process. Finally, we provide theoretical and experimental evidence for the existence of a poly-logarithmic bound on the expected stabilization time of the majority model.

Are you Curious? Predicting the Human Curiosity from Facebook

2017 ◽  
Vol 25 (Suppl. 2) ◽  
pp. 79-95
Author(s):  
Alan Menk ◽  
Laura Sebastia
Keyword(s):  
Social Networks ◽  
Personality Traits ◽  
Real World ◽  
Virtual World ◽  
Prediction Models ◽  
Huge Amount ◽  
Human Personality ◽  
The Real ◽  
The Individual

Nowadays, social networks are daily used to share what people like, feel, where they travel to, etc. This huge amount of data can say a lot about their personality because it may reect their behaviour from the “real world” to the “virtual world”. Once obtained the access to this data, some authors have tried to infer the personality of the individual without the use of long questionnaires, only working with data in an implicit way, that is, transparently to the user. In this scenario, our work is focused on predicting one of the human personality traits, the Curiosity. In this paper, we analyse the information that can be extracted from the users’ profile on Facebook and the set of features that can be used to describe their degree of curiosity. Finally, we use these data to generate several prediction models. The best generated model is able to predict the degree of curiosity with an accuracy of 87%.

scholarly journals A Mathematical Approach on Representation of Competitions: Competition Cluster Hypergraphs

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Sovan Samanta ◽  
G. Muhiuddin ◽  
Abdulaziz M. Alanazi ◽  
Kousik Das
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Real World ◽  
Mathematical Approach ◽  
Numerical Application ◽  
The Real ◽  
Or Groups ◽  
Mixed Graphs

Social networks are represented using graph theory. In this case, individuals in a social network are assumed as nodes. Sometimes institutions or groups are also assumed as nodes. Institutions and such groups are assumed as cluster nodes that contain individuals or simple nodes. Hypergraphs have hyperedges that include more than one node. In this study, cluster hypergraphs are introduced to generalize the concept of hypergraphs, where cluster nodes are allowed. Sometimes competitions in the real world are done as groups. Cluster hypergraphs are used to represent such kinds of competitions. Competition cluster hypergraphs of semidirected graphs (a special type of mixed graphs called semidirected graphs, where the directed and undirected edges both are allowed) are introduced, and related properties are discussed. To define competition cluster hypergraphs, a few properties of semidirected graphs are established. Some associated terms on semidirected graphs are studied. At last, a numerical application is illustrated.

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