Social networks contain implicit knowledge that can be used to infer hierarchical relations that are not explicitly present in the available data. Interaction patterns are typically affected by users’ social relations. We present an approach to inferring such information that applies a link-analysis ranking algorithm at different levels of time granularity. In addition, a voting scheme is emplo...

Social networks are gaining an increasing popularity on the internet and are becoming a critical media for business and marketing. Hence, it is important to identify the key users that may play a critical role as sources or targets of content dissemination. Existing approaches rely only on users social connections; however, considering a single kind of information does not guarantee satisfactor...

The rapid growth of Geo-Social Networks (GeoSNs) provides a new and rich form of data. Users of GeoSNs can capture their geographic locations and share them with other users via an operation named checkin. Thus, GeoSNs can track the connections (and the time of these connections) of geographic data to their users. In addition, the users are organized in a social network, which can be extended t...

Empirical data on early network history are rare. Students beginning their studies at a university with no or few prior connections to each other offer a unique opportunity to investigate the formation and early development of social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solvin...

The study of communities in social networks has attracted considerable interest from many disciplines. Most studies have focused on static networks, and in doing so, have neglected the temporal dynamics of the networks and communities. This paper considers the problem of tracking communities over time in dynamic social networks. We propose a method for community tracking using an adaptive evolu...

Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization. We adapt this general method to the context of social networks, extend it wit...

The growing availability of social media platforms, in particular microblogs such as Twitter, opened new way to people for expressing their opinions. Sentiment Analysis aims at inferring the polarity of these opinions, but most of the existing approaches are based only on text, disregarding information that comes from the relationships among users and posts. In this paper we consider microblogs...

We examine how interaction choices depend on the interplay of social and physical distance, and show that agents who are more central in the social network, or are located closer to the geographic center of interaction, choose higher levels of interactions in equilibrium. As a result, the level of interactivity in the economy as a whole will rise with the density of links in the social network ...

Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper, we use combination of Formal Concept Analysis and well-known matrix factorization methods to address computational complexity of social networks analysis and clarity of their visualization. The goal is to reduce the dimension of s...

We propose a new link-injection method aiming at boosting the overall diffusion of information in social networks. Our approach is based on a diffusion-coverage score of the ability of each user to spread information over the network. Candidate links for injection are identified by a matrix factorization technique and link injection is performed by attaching links to users according to their sc...