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We introduce a reshuffled approach to empirical analyze signs' organization in real directed signed social networks of Epinions and Slashdots from the global viewpoint. In the reshuffled approach, each negative link has probability $p_{rs}$…

物理与社会 · 物理学 2016-06-02 Long Guo , Fujuan Gao

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between…

社会与信息网络 · 计算机科学 2014-08-29 Dongjin Song , David A. Meyer

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in…

物理与社会 · 物理学 2010-03-15 Jure Leskovec , Daniel Huttenlocher , Jon Kleinberg

Recent studies from social, biological, and engineering network systems have drawn attention to the dynamics over signed networks, where each link is associated with a positive/negative sign indicating trustful/mistrustful,…

社会与信息网络 · 计算机科学 2016-11-15 Guodong Shi , Alexandre Proutiere , Mikael Johansson , John S. Baras , Karl H. Johansson

We study asymptotic dynamical patterns that emerge among a set of nodes that interact in a dynamically evolving signed random network. Node interactions take place at random on a sequence of deterministic signed graphs. Each node receives…

社会与信息网络 · 计算机科学 2013-09-24 Guodong Shi , Alexandre Proutiere , Mikael Johansson , John. S. Baras , Karl H. Johansson

Studying significant network patterns, known as graphlets (or motifs), has been a popular approach to understand the underlying organizing principles of complex networks. Statistical significance is routinely assessed by comparing to null…

物理与社会 · 物理学 2024-11-08 Bingjie Hao , István A. Kovács

The Ego Network Model (ENM) is a model for the structural organisation of relationships, rooted in evolutionary anthropology, that is found ubiquitously in social contexts. It takes the perspective of a single user (Ego) and organises their…

社会与信息网络 · 计算机科学 2024-01-31 Jack Tacchi , Chiara Boldrini , Andrea Passarella , Marco Conti

The Ego Network Model (ENM) describes how individuals organise their social relations in concentric circles (typically five) of decreasing intimacy, and it has been found almost ubiquitously in social networks, both offline and online. The…

社会与信息网络 · 计算机科学 2022-12-02 Jack Tacchi , Chiara Boldrini , Andrea Passarella , Marco Conti

Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social networks that has focused almost exclusively on positive…

物理与社会 · 物理学 2010-03-15 Jure Leskovec , Daniel Huttenlocher , Jon Kleinberg

The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in…

We present measures, models and link prediction algorithms based on the structural balance in signed social networks. Certain social networks contain, in addition to the usual 'friend' links, 'enemy' links. These networks are called signed…

社会与信息网络 · 计算机科学 2014-02-28 Jérôme Kunegis

The massive amounts of data that social media generates has facilitated the study of online human behavior on a scale unimaginable a few years ago. At the same time, the much discussed apparent randomness with which people interact online…

计算机与社会 · 计算机科学 2012-07-23 Chunyan Wang , Bernardo A. Huberman

In a large social network whose members harbor binary sentiments towards an issue, we investigate the asymptotic accuracy of sentiment detection. We model the user sentiments by an Ising Markov random field model and allow the user…

社会与信息网络 · 计算机科学 2017-10-10 Tian Tong , Rohit Negi

Research in network science has shown that many naturally occurring and technologically constructed networks are scale free, that means a power law degree distribution emerges from a growth model in which each new node attaches to the…

物理与社会 · 物理学 2009-11-11 Michael Schnegg

Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with…

社会与信息网络 · 计算机科学 2012-12-04 Yanhua Li , Wei Chen , Yajun Wang , Zhi-Li Zhang

The entropy of network ensembles characterizes the amount of information encoded in the network structure, and can be used to quantify network complexity, and the relevance of given structural properties observed in real network datasets…

无序系统与神经网络 · 物理学 2014-06-18 Kartik Anand , Dimitri Krioukov , Ginestra Bianconi

Randomized experiments, or A/B tests are used to estimate the causal impact of a feature on the behavior of users by creating two parallel universes in which members are simultaneously assigned to treatment and control. However, in social…

社会与信息网络 · 计算机科学 2019-02-20 Craig Tutterow , Guillaume Saint-Jacques

We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or…

社会与信息网络 · 计算机科学 2015-08-17 Guodong Shi , Alexandre Proutiere , Mikael Johansson , John S. Baras , Karl H. Johansson

Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as…

社会与信息网络 · 计算机科学 2020-01-07 Ghazaleh Beigi , Jiliang Tang , Huan Liu

We analyse the corpus of user relationships of the Slashdot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative…

社会与信息网络 · 计算机科学 2017-11-01 Jérôme Kunegis , Andreas Lommatzsch , Christian Bauckhage
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