Matteo Magnani

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Due to their large worldwide adoption, Social Network Sites (SNSs) have been widely used in many global events as an important source to spread news and information. While the searchability and persistence of this information make it ideal for sociological research, a quantitative approach is still challenging because of the size and complexity of the data.(More)
In the last few years, uncertainty management has come to be recognized as a fundamental aspect of data integration. It is now accepted that it may not be possible to remove uncertainty generated during data integration processes and that uncertainty in itself may represent a source of relevant information. Several issues, such as the aggregation of(More)
Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena(More)
Uncertainty is an intrinsic feature of automatic and semiautomatic data integration processes. Although many solutions have been proposed to reduce uncertainty, if we do not explicitly represent and keep it up to the end of the integration process we risk to lose relevant information, and to produce misleading results. Models for uncertain data can then be(More)
The design of business processes involves the usage of modeling languages, tools and methodologies. In this paper we highlight and address a relevant limitation of the Business Process Modeling Notation (BPMN): its weak data representation capabilities. In particular, we extend it with data-specific constructs derived from existing data modeling notations(More)
In this paper we introduce a new model to represent an interconnected network of networks. This model is fundamental to reason about the real organization of on-line social networks, where users belong to and interact on different networks at the same time. In addition we extend traditional SNA measures to deal with this multiplicity of networks and we(More)
Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial(More)
In this article we introduce a novel search paradigm for microblogging sites resulting from the intersection of Information Retrieval and Social Network Analysis (SNA). This approach is based on a formal model of on-line conversations and a set of ranking measures including SNA centrality metrics, time-related conversational metrics and other specific(More)