Uncovering community structure is a core challenge in social network analysis. This is a significant challenge for large networks where there is a single type of relation in the network (e.g. friend or knows). In practice ...
We apply methods from social network analysis and visualization to facilitate a study of the Irish blogosphere from a cultural studies perspective. We focus on solving the practical issues that arise when the goal is to ...
Network modeling can be approached using either discriminative or probabilistic
models. In the task of link prediction a probabilistic model will give a probability
for the existence of a link; while in some scenarios ...
Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, ...
The analysis of network data is an area that is rapidly growing, both within and outside of the discipline of statistics.
This review provides a concise summary of methods and models used in the statistical analysis of ...
Many recent approaches to modeling social networks have focussed on embedding
the actors in a latent “social space”. Links are more likely for actors that are
close in social space than for actors that are distant in ...
Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus ...
As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of ...
Throughout the last decade the ‘creative class’ thesis has received significant attention within academic and policymaking circles. This paper analyses the role of the ‘creative class’ thesis within recent urban and economic ...