Yunkyu Sohn

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one perso

The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as

Cognitive scientists, behavior geneticists, and political scientists have identified several ways in which emotions influence political attitudes, and psychologists have shown that emotion regulation can have an important causal effect on physiology, cognition, and subjective experience. However, no work to date explores the possibility that emotion regulation may shape political ideology and attitudes toward policies. Here, we conduct four studies that investigate the role of a particular emoti

Abstract Dense cooperative networks are an essential element of social capital for prosperous societies. These networks enable individuals to overcome collective action dilemmas by enhancing trust. In many biological and social settings, network structures evolve endogenously as agents exit relationships and build new ones. However, the interplay between game strategy and interaction structure by which evolutionary dynamics leads to self-organization of dense cooperative networks has not been un

Abstract Network scholars commonly encounter multiple networks, each of which is possibly governed by distinct generation rules while sharing a node group structure. Although the stochastic blockmodeling—detecting such latent group structures with group-specific connection profiles—has been a major topic of recent research, the focus has been given to the assortative group discovery of a single network. Despite its universality, concepts, and techniques for simultaneous characterization of node

Social network services (SNSs) are now the primary advertisingmedium in terms of both reach and engagement.For both businesses and the SNS providers, it iscrucial to find advertising methods that users perceiveto be valuable. In this paper, we provide an empiricalevidence for the role of different advertising methodson SNSs (i.e. earned vs. paid) on the subjective evaluationof the relative worth of advertising. In particular,we concentrate on the act of ‘friend tagging’ – the conventionof taggin

Dynamic modeling of longitudinal networks has been an increasingly important topic in applied research. While longitudinal network data commonly exhibit dramatic changes in its structures, existing methods have largely focused on modeling smooth topological changes over time. In this paper, we develop a hidden Markov network change-point model (HNC) that combines the multilinear tensor regression model (Hoff, 2011) with a hidden Markov model using Bayesian inference. We model changes in network

Dense cooperative networks are an essential element of social capital for a prosperous society. These networks enable individuals to overcome collective action dilemmas by enhancing trust. In many biological and social settings, network structures evolve endogenously as agents exit relationships and build new ones. However, the process by which evolutionary dynamics lead to self-organization of dense cooperative networks has not been explored. Our large group prisoner's dilemma experiments with

인공지능 기술의 급격한 발전에 따라 알고리즘의 사회 적용이 일반화되면서, 규범적 가치, 특히 공정성 기준을 어떻게 자동화된 의사결정 구조에 반영할지가 인공지능 거버넌스의 중요한 의제로 부상하고 있다. 이 논문의 일차적 목표는 알고리즘 공정성에 대한 피상적인 논의에서 벗어나, 해당 주제의주요 개념과 근본 문제를 소개하고, 기초적인 통계 지식으로 이해 가능한 사례를 제시하는 것이다. 알고리즘 사회 적용 파이프라인의 구조, 공정성 개념의 종류와 이들의 관계, 규범적 기준을 자동화된 의사결정에 반영하는 방법을 다루고, 이를 통해 시대적 요구에 발맞춘 진전된 논의를 도모한다. 나아가, 사회과학이 개척해야 할 연구 영역과 발전 방향은 무엇이 될 수 있는지 알아보고, 앞으로의 사회과학은 기존의 회고적 학문에서 실천적 학문으로 나아가야 함을 주장한다.

Abstract Fluxus is often understood as an avant-garde art movement led by George Maciunas in the 1960s. Such a narrative, however, is limiting as it overlooks the contribution of other prominent Fluxus artists. This article aims to challenge what is referred to as the Maciunas-based paradigm in its temporal scope and ideological homogeneity through the adoption of social network analysis.

This dissertation introduces a set of new statistical methods for measuring foundational constructs in political science: ideology, dimensionality and polarization. Using the proposed methods, I offer novel findings on multidimensional ideological characteristics of American Congress. The final chapter provides the first complete description of ideological coalitions in Korean National Assembly from its birth to the present by introducing a new structural model of ideal point estimation for non-

For decades, scholars of various disciplines have been fretted over strategic interactions, presenting theoretical insights and empirical observations. Despite the central role played by strategic interactions in creating values in the Internet environment, our ability to understand them scientifically and to manage them in practice has remained limited. While engineering communities suffer from not having enough theoretical resource to formalize such phenomena, economics and social sciences lac

The existence of anatomical basis of functional modularity in the brain has been postulated. Previous studies in the field of theoretical neuroanatomy claimed the segregated nature of cortical areas with their physiological functions by analyzing fiber connection data of mammalian brains. The aim of this study was to investigate, using modularity-based spectral community (cluster) detection algorithm, whether the behavioral-level circuits of Caenorhabditis elegans (C. elegans), which is the only

사회 네트워크 분석은 사회현상을 설명하는 데에 상호작용 구조의 특성을 중심에 둔다. 이는 사회과학자들이 관심 있어 하는 대부분의 개념, 사회경제적 지위와 심리적 상태 등이 행위자들 간의 경쟁, 협력 혹은 착취의 결과로서 존재하기 때문이다. 하지만 실세계 상호작용의 다양한 양상에도 불구하고, 그래프 이론에 기반한 대부분의 구조적 분석은 암묵적으로 매우 간단한 형태의 네트워크에서의 단순한 동역학적 가정을 기반으로 정당화되어왔다. 이 논문에서는 방향성, 가중치, 부호, 자기연결이 모두 존재하는 상호작용 일반에 대한 사회 네트워크 동역학 모형으로서 이론 생태학의 로트카-볼테라(Lotka-Volterra) 모형이 쓰일 수 있음을 제시한다. 그리고 이 모형을 이용해서 얻은 상호작용의 구조적 특성과 계의 안정성과의 보편적 관계에 대한 최신 분석 결과를 중심으로 사회 네트워크 연구에서의 의미와 활용 가능성을 논한다.

인공지능 기술의 급격한 발전에 따라 알고리즘의 사회 적용이 일반화되면서, 규범적 가치, 특히 공정성 기준을 어떻게 자동화된 의사결정 구조에 반영할지가 인공지능 거버넌스의 중요한 의제로 부상하고 있다. 이 논문의 일차적 목표는 알고리즘 공정성에 대한 피상적인 논의에서 벗어나, 해당 주제의 주요 개념과 근본 문제를 소개하고, 기초적인 통계 지식으로 이해 가능한 사례를 제시하는 것이다. 알고리즘 사회 적용 파이프라인의 구조, 공정성 개념의 종류와 이들의 관계, 규범적 기준을 자동화된 의사결정에 반영하는 방법을 다루고, 이를 통해 시대적 요구에 발맞춘 진전된 논의를 도모한다. 나아가, 사회과학이 개척해야 할 연구 영역과 발전 방향은 무엇이 될 수 있는지 알아보고, 앞으로의 사회과학은 기존의 회고적 학문에서 실천적 학문으로 나아가야 함을 주장한다.