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  • 1.
    Ashcroft, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Vega, Davide
    Univ Bologna, Bologna, Italy..
    Montesi, Danilo
    Univ Bologna, Bologna, Italy..
    Rossi, Luca
    IT Univ Copenhagen, Copenhagen, Denmark..
    Multilayer Analysis of Online Illicit Marketplaces2016In: 2016 European Intelligence And Security Informatics Conference (EISIC) / [ed] Brynielsson, J Johansson, F, IEEE , 2016, p. 199-199Conference paper (Refereed)
  • 2. Atzmueller, Martin
    et al.
    Gaito, Sabrina
    Interdonato, Roberto
    Kanawati, Rushed
    Largeron, Christine
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Sala, Alessandra
    International Workshop on Mining Attributed Networks (MATNET 2018) Chairs’ Welcome2018Other (Other academic)
  • 3. Bothorel, Cecile
    et al.
    Cruz, Juan David
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Micenkova, Barbora
    Clustering attributed graphs: models, measures and methods2015In: Network Science, ISSN 2050-1242, Vol. 3, no 3, p. 408-444Article in journal (Refereed)
    Abstract [en]

    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 representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

  • 4.
    Brodka, Piotr
    et al.
    Wroclaw Univ Sci & Technol, Fac Comp Sci & Management, Dept Computat Intelligence, Wroclaw, Poland.
    Chmiel, Anna
    Warsaw Univ Technol, Fac Phys, Warsaw, Poland.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Ragozini, Giancarlo
    Univ Naples Federico II, Dept Polit Sci, Naples, Campania, Italy.
    Quantifying layer similarity in multiplex networks: a systematic study2018In: Royal Society Open Science, E-ISSN 2054-5703, Vol. 5, no 8, article id 171747Article in journal (Refereed)
    Abstract [en]

    Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.

  • 5.
    Bøgh, Kenneth S
    et al.
    Aarhus University.
    Assent, Ira
    Aarhus University.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Efficient GPU-based skyline computation2013In: Proceedings of the Ninth International Workshop on Data Management on New Hardware (DaMoN @ SIGMOD), 2013Conference paper (Refereed)
    Abstract [en]

    The skyline operator for multi-criteria search returns the most interesting points of a data set with respect to any monotone preference function. Existing work has almost exclusively focused on efficiently computing skylines on one or more CPUs, ignoring the high parallelism possible in GPUs. In this paper we investigate the challenges for efficient skyline algorithms that exploit the computational power of the GPU. We present a novel strategy for managing data transfer and memory for skylines using CPU and GPU. Our new sorting based data-parallel skyline algorithm is introduced and its properties are discussed. We demonstrate in a thorough experimental evaluation that this algorithm is faster than state-of-the-art sequential sorting based skyline algorithms and that it shows superior scalability.

  • 6. Cozza, Vittoria
    et al.
    Messina, Antonio
    Montesi, Danilo
    Arietta, Luca
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Spatio-Temporal Keyword Queries in Social Networks2013In: 17th East-European Conference on Advances in Databases and Information Systems (ADBIS), 2013, p. 70-83Conference paper (Refereed)
    Abstract [en]

    Due to the large amount of social network data produced at an ever growing speed and their complex nature, recent works have addressed the problem of efficiently querying such data according to social, temporal or spatial dimensions. In this work we propose a data model that keeps into account all these dimensions and we compare different approaches for efficient query execution on a large real dataset using standard relational technologies.

  • 7.
    Fatemi, Zahra
    et al.
    Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Salehi, Mostafa
    Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
    A generalized force-directed layout for multiplex sociograms2018In: Social Informatics: 10th International Conference, SocInfo 2018, St. Petersburg, Russia, September 25-28, 2018, Proceedings, Part I, Springer, 2018, Vol. 11185, p. 212-227Conference paper (Refereed)
    Abstract [en]

    Multiplex networks are defined by the presence of multiple edge types. As a consequence, it is hard to produce a single visualization of a network revealing both the structure of each edge type and their mutual relationships: multiple visualization strategies are possible, depending on how each edge type should influence the position of the nodes in the sociogram. In this paper we introduce multiforce, a force-directed layout for multiplex networks where both intra-layer and inter-layer relationships among nodes are used to compute node coordinates. Despite its simplicity, our algorithm can reproduce the main existing approaches to draw multiplex sociograms, and also supports a new intermediate type of layout. Our experiments on real data show that multiforce enables layered visualizations where each layer represents an edge type, nodes are well aligned across layers and the internal layout of each layer highlights the structure of the corresponding edge type.

  • 8.
    Ghariblou, Saeed
    et al.
    Univ Tehran, Fac New Sci & Technol, Tehran, Iran; Inst Res Fundamental Sci IPM, Sch Comp Sci, Tehran, Iran.
    Salehi, Mostafa
    Univ Tehran, Fac New Sci & Technol, Tehran, Iran; Inst Res Fundamental Sci IPM, Sch Comp Sci, Tehran, Iran.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Jalili, Mahdi
    RMIT Univ, Sch Engn, Melbourne, Vic, Australia.
    Shortest Paths in Multiplex Networks2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 2142Article in journal (Refereed)
    Abstract [en]

    The shortest path problem is one of the most fundamental networks optimization problems. Nowadays, individuals interact in extraordinarily numerous ways through their offline and online life (e.g., co-authorship, co-workership, or retweet relation in Twitter). These interactions have two key features. First, they have a heterogeneous nature, and second, they have different strengths that are weighted based on their degree of intimacy, trustworthiness, service exchange or influence among individuals. These networks are known as multiplex networks. To our knowledge, none of the previous shortest path definitions on social interactions have properly reflected these features. In this work, we introduce a new distance measure in multiplex networks based on the concept of Pareto efficiency taking both heterogeneity and weighted nature of relations into account. We then model the problem of finding the whole set of paths as a form of multiple objective decision making and propose an exact algorithm for that. The method is evaluated on five real-world datasets to test the impact of considering weights and multiplexity in the resulting shortest paths. As an application to find the most influential nodes, we redefine the concept of betweenness centrality based on the proposed shortest paths and evaluate it on a real-world dataset from two-layer trade relation among countries between years 2000 and 2015.

  • 9.
    Hanteer, Obaida
    et al.
    IT University Of Copenhagen, DssLab, Denmark.
    Rossi, Luca
    IT University Of Copenhagen, DssLab, Denmark.
    Vega, Davide
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    From Interaction to Participation: The Role of the Imagined Audience in Social Media Community Detection and an Application to Political Communication on Twitter2018In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE Computer Society , 2018, p. 531-534Conference paper (Refereed)
    Abstract [en]

    In the context of community detection in online social media, a lot of effort has been put into the definition of sophisticated network clustering algorithms and much less on the equally crucial process of obtaining high-quality input data. User-interaction data explicitly provided by social media platforms has largely been used as the main source of data because of its easy accessibility. However, this data does not capture a fundamental and much more frequent type of participatory behavior where users do not explicitly mention others but direct their messages to an invisible audience following a common hashtag. In the context of multiplex community detection, we show how to construct an additional data layer about user participation not relying on explicit interactions between users, and how this layer can be used to find different types of communities in the context of Twitter political communication.

  • 10.
    Kaveh, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Rohner, Christian
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication.
    Comparing node degrees in probabilistic networks2019In: Journal of Complex Networks, E-ISSN 2051-1329, Vol. 7Article in journal (Refereed)
  • 11.
    Kaveh, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Rohner, Christian
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication.
    Defining and measuring probabilistic ego networks2019In: Applied Network Science Journal, E-ISSN 2364-8228Article in journal (Other academic)
  • 12. Lambertini, Mattia
    et al.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Marzolla, Moreno
    Montesi, Danilo
    Paolino, Carmine
    Large-Scale Social Network Analysis2014In: Large-Scale Data Analytics, Springer, 2014, p. 155-187Chapter in book (Refereed)
  • 13.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Assent, Ira
    Aarhus University.
    From stars to galaxies: skyline queries on aggregate data2013In: Proceedings of the 16th International Conference on Extending Database Technology, 2013, p. 477-488Conference paper (Refereed)
    Abstract [en]

    The skyline operator extracts relevant records from multidimensional databases according to multiple criteria. This operator has received a lot of attention because of its ability to identify the best records in a database without requiring to specify complex parameters like the relative importance of each criterion. However, it has only been defined with respect to single records, while one fundamental functionality of database query languages is aggregation, enabling operations over sets of records. In this paper we introduce aggregate skylines, where the skyline works as a filtering predicate on sets of records. This operator can be used to express queries in the form: return the best groups depending on the features of their elements, and thus provides a powerful combination of grouping and skyline functionality. We define a semantics for aggregate skylines based on a sound theoretical framework and study its computational complexity. We propose efficient algorithms to implement this operator and test them on real and synthetic data, showing that they outperform a direct SQL implementation of up to two orders of magnitude.

  • 14.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Assent, Ira
    Aarhus University.
    Hornbæ k, Kasper
    University of Copenhagen.
    Jakobsen, Mikkel R.
    University of Copenhagen.
    Larsen, Ken Friis
    University of Copenhagen.
    SkyView: a user evaluation of the skyline operator2013In: Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), 2013, p. 2249-2254Conference paper (Refereed)
    Abstract [en]

    The skyline operator has recently emerged as an alternative to ranking queries and retrieves a number of potential best options for arbitrary monotone preference functions. The success of this operator in the database community is based on the belief that users benefit from the limited effort required to specify skyline queries with respect to, for instance, ranking. However, while application examples exist and research on efficient computation in a variety of settings abounds, there exists no principled analysis of its benefits and limitations in data retrieval tasks. Our study investigates the degree to which users understand skyline queries, how they specify query parameters and how they interact with skyline results made available in listings or map-based interfaces.

  • 15.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Assent, Ira
    Mortensen, Michael L.
    Taking the Big Picture: Representative Skylines based on Significance and Diversity2014In: The VLDB journal, ISSN 1066-8888, E-ISSN 0949-877X, Vol. 23, no 5, p. 795-815Article in journal (Refereed)
    Abstract [en]

    The skyline is a popular operator to extract records from a database when a record scoring function is not available. However, the result of a skyline query can be very large. The problem addressed in this paper is the automatic selection of a small number of representative skyline records. Existing approaches have only focused on partial aspects of this problem. Some try to identify sets of diverse records giving an overall approximation of the skyline. These techniques, however, are sensitive to the scaling of attributes or to the insertion of non-skyline records into the database. Others exploit some knowledge of the record scoring function to identify the most significant record, but not sets of records representative of the whole skyline. In this paper, we introduce a novel approach taking both the significance of all the records and their diversity into account, adapting to available knowledge of the scoring function, but also working under complete ignorance. We show the intractability of the problem and present approximate algorithms. We experimentally show that our approach is efficient, scalable and that it improves existing works in terms of the significance and diversity of the results.

  • 16.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Marzolla, Moreno
    University of Bologna.
    Path-based and Whole Network Measures2014In: Encyclopedia of Social Network Analysis and Mining, Berlin: Springer , 2014Chapter in book (Refereed)
  • 17.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Monreale, Anna
    ISTI, CNR.
    Rossetti, Giulio
    ISTI, CNR.
    Giannotti, Fosca
    ISTI, CNR.
    On multidimensional network measures2013In: Italian Conference on Sistemi Evoluti per le Basi di Dati (SEBD), 2013Conference paper (Refereed)
  • 18.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Montesi, Danilo
    University of Bologna.
    Rossi, Luca
    IT University, Copenhagen.
    Factors Enabling Information Propagation in a Social Network Site2013In: The Influence of Technology on Social Network Analysis and Mining, Springer Vienna , 2013, p. 411-426Chapter in book (Refereed)
    Abstract [en]

    A relevant feature of Social Network Sites is their ability to propagate units of information and create large distributed conversations. This phenomenon is particularly relevant because of the speed of information propagation, which is known to be much faster than within traditional media, and because of the very large amount of people that can potentially be exposed to information items. While many general formal models of network propagation have been developed in different research fields, in this chapter we present the result of an empirical study on a Large Social Database (LSD) aimed at measuring specific socio-technical factors enabling information spreading in Social Network Sites.

  • 19.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Rossi, Luca
    IT University, Copenhagen.
    Formation of multiple networks2013In: Social Computing, Behavioral-Cultural Modeling and Prediction, Springer Berlin Heidelberg , 2013, p. 257-264Conference paper (Refereed)
    Abstract [en]

    While most research in Social Network Analysis has focused on single networks, the availability of complex on-line data about individuals and their mutual heterogenous connections has recently determined a renewed interest in multi-layer network analysis. To the best of our knowledge, in this paper we introduce the first network formation model for multiple networks. Network formation models are among the most popular tools in traditional network studies, because of both their practical and theoretical impact. However, existing models are not sufficient to describe the generation of multiple networks. Our model, motivated by an empirical analysis of real multi-layered network data, is a conservative extension of single-network models and emphasizes the additional level of complexity that we experience when we move from a single- to a more complete and realistic multi-network context.

  • 20.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Rossi, Luca
    IT University, Copenhagen.
    Multiple social networks, data models and measures for2014In: Encyclopedia of Social Network Analysis and Mining, Berlin: Springer, 2014Chapter in book (Refereed)
  • 21.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Rossi, Luca
    IT University, Copenhagen.
    Pareto Distance for Multi-layer Network Analysis2013In: Social Computing, Behavioral-Cultural Modeling and Prediction / [ed] Ariel M. Greenberg, William G. Kennedy, Nathan D. Bos, 2013, Vol. 7812, p. 249-256Conference paper (Refereed)
    Abstract [en]

    Social Network Analysis has been historically applied to single networks, e.g., interaction networks between co-workers. However, the advent of on-line social network sites has emphasized the stratified structure of our social experience. Individuals usually spread their identities over multiple services, e.g., Facebook, Twitter, LinkedIn and Foursquare. As a result, the analysis of on-line social networks requires a wider scope and, more technically speaking, models for the representation of this fragmented scenario. The recent introduction of more realistic layered models has however determined new research problems related to the extension of traditional single-layer network measures. In this paper we take a step forward over existing approaches by defining a new concept of geodesic distance that includes heterogeneous networks and connections with very limited assumptions regarding the strength of the connections. This is achieved by exploiting the concept of Pareto efficiency to define a simple and at the same time powerful measure that we call Pareto distance, of which geodesic distance is a particular case when a single layer (or network) is analyzed. The limited assumptions on the nature of the connections required by the Pareto distance may in theory result in a large number of potential shortest paths between pairs of nodes. However, an experimental computation of distances on multi-layer networks of increasing size shows an interesting and non-trivial stable behavior.

  • 22.
    Magnani, Matteo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Wasserman, Stanley
    Indiana Univ, USA; Natl Res Univ, Higher Sch Econ, Moscow, Russia..
    Introduction to the special issue on multilayer networks2017In: NETWORK SCIENCE, ISSN 2050-1242, Vol. 5, no 2, p. 141-143Article in journal (Other academic)
  • 23.
    Medina, Esunly
    et al.
    Univ Politecn Cataluna, Dept Comp Architecture, Barcelona, Spain.
    Vega, Davide
    Univ Politecn Cataluna, Dept Comp Architecture, Barcelona, Spain.
    Meseguer, Roc
    Univ Politecn Cataluna, Dept Comp Architecture, Barcelona, Spain.
    Medina, Humberto
    Coventry Univ, Dept Aerosp Elect & Elect Engn, Coventry, W Midlands, England.
    Ochoa, Sergio F.
    Univ Chile, Dept Comp Sci, Santiago, Chile.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Using indirect blockmodeling for monitoring students roles in collaborative learning networks2016In: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) / [ed] Shen, W; Liu, X; Yang, C; Barthes, JP; Luo, J; Chen, L; Yong, J, 2016, p. 164-169Conference paper (Refereed)
    Abstract [en]

    Collaborative learning activities have shown to be useful to address educational processes in several contexts. Monitoring these activities is mandatory to determine the quality of the collaboration and learning processes. Recent research works propose using Social Network Analysis techniques to understand students' collaboration learning process during these experiences. Aligned with that, this paper proposes the use of the indirect blockmodeling network analytic technique for monitoring the behaviour of different social roles played by students in collaborative learning scenarios. The usefulness of this technique was evaluated through a study that analysed the students' interaction network in a collaborative learning activity. Particularly, we tried to understand the structure of the interaction network during that process. Preliminary results suggest that indirect blockmodeling is highly useful for inferring and analysing the students' social roles, when the behaviour of roles are clearly different among them. This technique can be used as a monitoring service that can be embedded in collaborative learning applications.

  • 24. Mortensen, Michael
    et al.
    Chester, Sean
    Assent, Ira
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Efficient caching for constrained skyline queries2015In: Extending Database Technology (EDBT), 2015Conference paper (Refereed)
  • 25.
    Musial, Katarzyna
    et al.
    Bournemouth Univ, Data Sci Inst, Fac Sci & Technol, Bournemouth, Dorset, England..
    Brodka, Piotr
    Wroclaw Univ Technol, Dept Computat Intelligence, PL-50370 Wroclaw, Poland..
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Social Network Analysis in Applications2016In: AI Communications, ISSN 0921-7126, E-ISSN 1875-8452, Vol. 29, no 1, p. 55-56Article in journal (Other academic)
  • 26.
    Ramezanian, Rasoul
    et al.
    Sharif Univ Technol, Tehran, Iran.
    Salehi, Mostafa
    Univ Tehran, Tehran 14174, Iran; Univ Bologna, I-40126 Bologna, Italy.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Montesi, Danilo
    Univ Bologna, I-40126 Bologna, Italy.
    Diffusion of Innovations over Multiplex Social Networks2015In: International Symposium on Artificial Intelligence and Signal Processing (AISP), 2015, p. 1-5Conference paper (Refereed)
    Abstract [en]

    The ways in which an innovation (e.g., new behaviour, idea, technology, product) diffuses among people can determine its success or failure. In this paper, we address the problem of diffusion of innovations over multiplex social networks where the neighbours of a person belong to one or multiple networks (or layers) such as friends, families, or colleagues. To this end, we generalise one of the basic game-theoretic diffusion models, called networked coordination game, for multiplex networks. We present analytical results for this extended model and validate them through a simulation study, finding among other properties a lower bound for the success of an innovation.While simple and leading to intuitively understandable results, to the best of our knowledge this is the first extension of a game-theoretic innovation diffusion model for multiplex networks and as such it provides a basic framework to study more sophisticated innovation dynamics.

  • 27. Rossi, Luca
    et al.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Towards effective visual analytics on multiplex and multilayer networks2015In: Chaos, Solitons & Fractals, ISSN 0960-0779, E-ISSN 1873-2887, Vol. 72, p. 68-76Article in journal (Refereed)
    Abstract [en]

    In this article we discuss visualisation strategies for multiplex networks. Since Moreno's early works on network analysis, visualisation has been one of the main ways to understand networks thanks to its ability to summarise a complex structure into a single representation highlighting multiple properties of the data. However, despite the large renewed interest in the analysis of multiplex networks, no study has proposed specialised visualisation approaches for this context and traditional methods are typically applied instead. In this paper we initiate a critical and structured discussion of this topic, and claim that the development of specific visualisation methods for multiplex networks will be one of the main drivers pushing current research results into daily practice.

  • 28. Salehi, Mostafa
    et al.
    Sharma, Rajesh
    Marzolla, Moreno
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Siyari, Payam
    Montesi, Danilo
    Spreading processes in Multilayer Networks2015In: IEEE Transactions on Network Science and Engineering, ISSN 2327-4697, Vol. 2, no 2, p. 65-83Article in journal (Refereed)
  • 29. Salehi, Mostafa
    et al.
    Siyari, Payam
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Montesi, Danilo
    Multidimensional epidemic thresholds in diffusion processes over interdependent networks2015In: Chaos, Solitons & Fractals, ISSN 0960-0779, E-ISSN 1873-2887, Vol. 72, p. 59-67Article in journal (Refereed)
    Abstract [en]

    Several systems can be modeled as sets of interdependent networks where each network contains distinct nodes. Diffusion processes like the spreading of a disease or the propagation of information constitute fundamental phenomena occurring over such coupled networks. In this paper we propose a new concept of multidimensional epidemic threshold characterizing diffusion processes over interdependent networks, allowing different diffusion rates on the different networks and arbitrary degree distributions. We analytically derive and numerically illustrate the conditions for multilayer epidemics, i.e., the appearance of a giant connected component spanning all the networks. Furthermore, we study the evolution of infection density and diffusion dynamics with extensive simulation experiments on synthetic and real networks.

  • 30.
    Sharma, Rajesh
    et al.
    Univ Bologna, Bologna, Italy..
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Montesi, Danilo
    Univ Bologna, Bologna, Italy..
    Effects of missing data in multilayer networks2016In: Social Network Analysis and Mining, ISSN 1869-5450, E-ISSN 1869-5469, Vol. 6, no 1Article in journal (Refereed)
    Abstract [en]

    A common problem in social network analysis is the presence of missing data. This problem has been extensively investigated in single-layer networks, that is, considering one network at a time. However, in multilayer networks, in which a holistic view of multiple networks is taken, the problem has not been specifically studied. In this work, we take an exhaustive and systematic approach to understand the effect of missing data in multilayer networks. Differently from the single-layer networks, depending on layer interdependencies, the common network properties can increase or decrease with respect to the properties of the complete network. Another important aspect we observed through our experiments on six real and eleven synthetic datasets is that multilayer network properties like layer correlation and relevance can be used to understand the impact of missing data compared to measuring traditional network measures.

  • 31. Sharma, Rajesh
    et al.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Montesi, Danilo
    Investigating the types and effects of missing data in multilayer networks2015In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015, p. 392-399Conference paper (Refereed)
    Abstract [en]

    A common problem in social network analysis isthe presence of missing data. This problem has been extensivelyinvestigated in single layer networks, that is, considering onenetwork at a time. However, in multilayer networks, in which aholistic view of multiple networks is taken, the problem has notbeen specifically studied, and results for single layer networks arereused with no adaptation. In this work, we take an exhaustiveand systematic approach to understand the effect of missingdata in multilayer networks. Differently from the single layernetworks, depending on layer interdependencies, the commonnetwork properties can increase or decrease with respect to theproperties of the complete network. Another important aspectwe observed through our experiments on real datasets is thatmultilayer network properties like layer correlation and relevancecan be used to understand the impact of missing data comparedto measuring traditional network measures.

  • 32.
    Sharma, Rajesh
    et al.
    University of Bologna.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Montesi, Danilo
    University of Bologna.
    Missing data in multiplex networks: a preliminary study2014Conference paper (Refereed)
    Abstract [en]

    A basic problem in the analysis of social networks is missing data. When a network model does not accurately capture all the actors or relation- ships in the social system under study, measures computed on the network and ultimately the final outcomes of the analysis can be severely distorted. For this reason, researchers in social network analysis have characterised the impact of different types of missing data on existing network measures. Recently a lot of attention has been devoted to the study of multiple-network systems, e.g., multiplex networks. In these systems missing data has an even more significant impact on the outcomes of the analyses. However, to the best of our knowledge, no study has focused on this problem yet. This work is a first step in the direction of understanding the impact of missing data in multiple networks. We first discuss the main reasons for missingness in these systems, then we explore the relation between various types of missing information and their effect on network properties. We provide initial experimental evidence based on both real and synthetic data. 

  • 33.
    Sharma, Rajesh
    et al.
    Univ Bologna, I-40126 Bologna, Italy..
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Montesi, Danilo
    Univ Bologna, I-40126 Bologna, Italy..
    Understanding community patterns in large attributed social networks2015In: Proceedings Of The 2015 IEEE/ACM International Conference On Advances In Social Networks Analysis And Mining (Asonam 2015), 2015, p. 1503-1508Conference paper (Refereed)
    Abstract [en]

    There is an inherent presence of communities in online social networks. These communities can be defined based on i) link structure or ii) the attributes of individuals. Attributes can indicate as interests in specific topics, like science-fiction books or romantic movies, or more in general their explicit affiliation to a group inside the network. In this paper, we analyze community structures as defined by how people are associated to third concepts like attributes. To understand the community patterns we analyze three large and one small social network datasets. Our analysis shows that, irrespective of the number of nodes for any particular interest in the network, at least 50% of the nodes are part of the same connected component in the graph induced by each interest. Another interesting result of our analysis is that the majority of sub-communities (50% or above) for any interest are separated by small hops (two to three) from each other.

  • 34.
    Tehrani, Nazanin Afsarmanesh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. Gavagai, Stockholm, Sweden.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. Gavagai, Stockholm, Sweden.
    Partial and Overlapping Community Detection in Multiplex Social Networks2018In: 10th International Conference on Social Informatics (SocInfo2018 ), Springer, 2018, Vol. 11186, p. 15-28Conference paper (Refereed)
    Abstract [en]

    We extend the popular clique percolation method to multiplex networks. Our extension requires to rethink the basic concepts on which the original clique percolation algorithm is based, including cliques and clique adjacency, to handle the presence of multiple types of ties. We also provide an experimental characterization of the communities that our method can identify.

  • 35.
    Vega, Davide
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Foundations of Temporal Text Networks2018In: Applied Network Science, Vol. 3, no 25Article in journal (Refereed)
    Abstract [en]

    Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.

  • 36. Vega, Davide
    et al.
    Meseguer, Roc
    Freitag, Felix
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Role and position detection in networks: reloaded2015In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015, p. 320-325Conference paper (Refereed)
    Abstract [en]

    Roles and positions are structural components in complex social systems which group actors based on how similarly they are connected to the rest of the actors. Role and position detection methods have been successfully used to evaluate and understand the dynamics of social networks and the behavior of their members. However, actor similarities used to detect positions have been based on pairwise comparisons so far: e.g., structural equivalence states that Alice and Bob are in the same position if they are both connected or not to the same other actors in the network, one by one. In this work we present a new framework to find positions and roles using comparisons between actors and sets of actors instead of just using pairwise comparisons. In this way we enable the usage of many more measures of similarity inside position and role detection methods, e.g., based on distances, community structure, triangles and cliques. As a result, we can identify new types of easily interpretable positions. Additionally, the proposed idea can be adapted to more complex models like hypergraphs or multiplex/multi-relational networks. We have evaluated our work on both synthetic and real data, using several existing and new similarity measures and providing both qualitative and quantitative evidence of the new possibilities enabled by our approach.

1 - 36 of 36
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