Amir Afrasiabi Rad

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Web services composition is an emerging paradigm for enabling inter and intra organizational integration, and a landscape of languages and techniques for modeling business processes in web service based environments has emerged and is continuously being enriched. With the advent of modeling standards, different business sectors are investigating the options(More)
Social networks and the propagation of content within social networks have received an extensive attention during the past few years. Social network content propagation is believed to depend on the similarity of users as well as on the existence of friends in the social network. Our former investigation of the YouTube social network showed that strangers(More)
We conducted a propagation analysis on an open social network, i.e., YouTube, by crawling one of its friendship networks and one of its subscribers (followers) networks. Our study is unique because it investigates the two main types of connections (i.e., friends and followers) within the same environment and interaction features. We observed that the effect(More)
The temporal component of social networks is often neglected in their analysis, and statistical measures are typically performed on a "static" representation of the network. As a result, measures of importance (like betweenness centrality) cannot reveal any temporal role of the entities involved. Our goal is to start filling this limitation by proposing a(More)
In terms of business process-modeling, healthcare is a rather complex sector of activity. Indeed, modeling healthcare processes presents some special requirements dictated by the complex and dynamic nature of these processes as well as by the specificity and diversity of the actors involved in these processes. We discuss these requirements and propose a(More)
Background Highly dynamic networks are networks where connectivity changes in time and connection patterns display possibly complex dynamics. Such networks are more and more pervasive in everyday life and the study of their properties is the object of extensive investigation in a Abstract Background: Highly dynamic social networks, where connectivity(More)
The temporal component of social networks is often neglected in their analysis, and statistical measures are usually performed on “static” network representations. As a result, measures of importance (like betweenness centrality) typically do not reveal the temporal role of the entities involved. Our goal is to contribute to fill this limitation by(More)
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