Published in Social Network Analysis and Mining
Unweighted network measures are commonly used to analyze real-world networks due to their simplicity and intuitiveness. This motivated the search for generalizations of unweighted network measures that take weights into account. We propose a new generalization methodology that captures how focused are the interactions over edges. The less focused t...
Published in Social Network Analysis and Mining
Civil unrest events (protests, strikes, and “occupy” events) range from small, nonviolent protests that address specific issues to events that turn into large-scale riots. Detecting and forecasting these events is of key interest to social scientists and policy makers because they can lead to significant societal and cultural changes. We forecast c...
Published in Social Network Analysis and Mining
The field of market basket analysis, the search for meaningful associations in customer purchase data, is one of the oldest areas of data mining. The typical solution involves the mining and analysis of association rules, which take the form of statements such as “people who buy diapers are likely to buy beer”. It is well-known, however, that typic...
Published in Social Network Analysis and Mining
In this paper, we introduce a method for analyzing the temporal dynamics of affiliation networks. We define affiliation groups which describe temporally related subsets of actors and describe an approach for exploring changing memberships in these affiliation groups over time. To model the dynamic behavior in these networks, we consider the concept...
Published in Social Network Analysis and Mining
Recommender systems are widely used for personalization of information on the Web and information retrieval systems. collaborative filtering (CF) is the most popular recommendation technique. However, classical CF (CCF) systems use only direct links and common features to model relationships between users. This paper presents a new densified behavi...
Published in Social Network Analysis and Mining
Triangle counting is an important problem in graph mining. The clustering coefficient and the transitivity ratio, two commonly used measures effectively quantify the triangle density in order to quantify the fact that friends of friends tend to be friends themselves. Furthermore, several successful graph-mining applications rely on the number of tr...
Published in Social Network Analysis and Mining
The science of social network analysis has co-evolved with the development of online environments and computer-mediated communication. Unique and precise data available from computer and information systems have allowed network scientists to explore novel social phenomena and develop new methods. Additionally, advances in the structural analysis an...
Published in Social Network Analysis and Mining
The purpose of this paper is to present an approach to model, analyze and visualize online production systems, such as Wikipedia, open source software development processes, and Flickr. While the final result of the production in these systems depends on the type of product (e.g., content, software, and tags), there are similarities in their modes ...
Published in Social Network Analysis and Mining
Large amounts of detailed transactional information are generated by ongoing social processes. For managing and mining such data, we treat them as “objects” and “relations”. These ideas strongly parallel the way that social network analysts conceive of social structure. Modality (roughly, distinguishing multiple classes of social actors or nodes in...
Published in Social Network Analysis and Mining
Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals who play important roles i...
