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Giulio Rossetti
ISTI-CNR, Pisa, Italy

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Research article
Published: 12 July 2021 in PLOS Computational Biology
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Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.

ACS Style

Ioanna Miliou; Xinyue Xiong; Salvatore Rinzivillo; Qian Zhang; Giulio Rossetti; Fosca Giannotti; Dino Pedreschi; Alessandro Vespignani. Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology 2021, 17, e1009087 .

AMA Style

Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani. Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology. 2021; 17 (7):e1009087.

Chicago/Turabian Style

Ioanna Miliou; Xinyue Xiong; Salvatore Rinzivillo; Qian Zhang; Giulio Rossetti; Fosca Giannotti; Dino Pedreschi; Alessandro Vespignani. 2021. "Predicting seasonal influenza using supermarket retail records." PLOS Computational Biology 17, no. 7: e1009087.

Article
Published: 17 June 2021 in Journal of Intelligent Information Systems
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Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate “what if” epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.

ACS Style

Giulio Rossetti; Letizia Milli; Salvatore Citraro; Virginia Morini. UTLDR: an agent-based framework for modeling infectious diseases and public interventions. Journal of Intelligent Information Systems 2021, 1 -22.

AMA Style

Giulio Rossetti, Letizia Milli, Salvatore Citraro, Virginia Morini. UTLDR: an agent-based framework for modeling infectious diseases and public interventions. Journal of Intelligent Information Systems. 2021; ():1-22.

Chicago/Turabian Style

Giulio Rossetti; Letizia Milli; Salvatore Citraro; Virginia Morini. 2021. "UTLDR: an agent-based framework for modeling infectious diseases and public interventions." Journal of Intelligent Information Systems , no. : 1-22.

Journal article
Published: 10 June 2021 in Applied Sciences
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In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users’ ideology on the controversy; (iii) the construction of users’ debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump’s presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time.

ACS Style

Virginia Morini; Laura Pollacci; Giulio Rossetti. Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study. Applied Sciences 2021, 11, 5390 .

AMA Style

Virginia Morini, Laura Pollacci, Giulio Rossetti. Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study. Applied Sciences. 2021; 11 (12):5390.

Chicago/Turabian Style

Virginia Morini; Laura Pollacci; Giulio Rossetti. 2021. "Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study." Applied Sciences 11, no. 12: 5390.

Journal article
Published: 13 January 2021 in IEEE Intelligent Systems
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Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.

ACS Style

Giulio Rossetti; Salvatore Citraro; Letizia Milli. Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks. IEEE Intelligent Systems 2021, 36, 25 -34.

AMA Style

Giulio Rossetti, Salvatore Citraro, Letizia Milli. Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks. IEEE Intelligent Systems. 2021; 36 (1):25-34.

Chicago/Turabian Style

Giulio Rossetti; Salvatore Citraro; Letizia Milli. 2021. "Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks." IEEE Intelligent Systems 36, no. 1: 25-34.

Research
Published: 10 June 2020 in Applied Network Science
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Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.

ACS Style

Giulio Rossetti. ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks. Applied Network Science 2020, 5, 1 -23.

AMA Style

Giulio Rossetti. ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks. Applied Network Science. 2020; 5 (1):1-23.

Chicago/Turabian Style

Giulio Rossetti. 2020. "ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks." Applied Network Science 5, no. 1: 1-23.

Conference paper
Published: 26 November 2019 in Econometrics for Financial Applications
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Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.

ACS Style

Salvatore Citraro; Giulio Rossetti. Eva: Attribute-Aware Network Segmentation. Econometrics for Financial Applications 2019, 141 -151.

AMA Style

Salvatore Citraro, Giulio Rossetti. Eva: Attribute-Aware Network Segmentation. Econometrics for Financial Applications. 2019; ():141-151.

Chicago/Turabian Style

Salvatore Citraro; Giulio Rossetti. 2019. "Eva: Attribute-Aware Network Segmentation." Econometrics for Financial Applications , no. : 141-151.

Conference paper
Published: 26 November 2019 in Econometrics for Financial Applications
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Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.

ACS Style

Giulio Rossetti. Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery. Econometrics for Financial Applications 2019, 152 -163.

AMA Style

Giulio Rossetti. Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery. Econometrics for Financial Applications. 2019; ():152-163.

Chicago/Turabian Style

Giulio Rossetti. 2019. "Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery." Econometrics for Financial Applications , no. : 152-163.

Conference paper
Published: 26 November 2019 in Econometrics for Financial Applications
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Viruses, opinions, ideas are different contents sharing a common trait: they need carriers embedded into a social context to spread. Modeling and approximating diffusive phenomena have always played an essential role in a varied range of applications from outbreak prevention to the analysis of meme and fake news. Classical approaches to such a task assume diffusion processes unfolding in a mean-field context, every actor being able to interact with all its peers. However, during the last decade, such an assumption has been progressively superseded by the availability of data modeling the real social network of individuals, thus producing a more reliable proxy for social interactions as spreading vehicles. In this work, following such a trend, we propose alternative ways of leveraging apriori knowledge on mesoscale network topology to design community-aware diffusion models with the aim of better approximate the spreading of content over complex and clustered social tissues.

ACS Style

Letizia Milli; Giulio Rossetti. Community-Aware Content Diffusion: Embeddednes and Permeability. Econometrics for Financial Applications 2019, 362 -371.

AMA Style

Letizia Milli, Giulio Rossetti. Community-Aware Content Diffusion: Embeddednes and Permeability. Econometrics for Financial Applications. 2019; ():362-371.

Chicago/Turabian Style

Letizia Milli; Giulio Rossetti. 2019. "Community-Aware Content Diffusion: Embeddednes and Permeability." Econometrics for Financial Applications , no. : 362-371.

Chapter
Published: 30 October 2019 in Social Dimensions of Organised Crime
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Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.

ACS Style

Remy Cazabet; Giulio Rossetti. Challenges in Community Discovery on Temporal Networks. Social Dimensions of Organised Crime 2019, 181 -197.

AMA Style

Remy Cazabet, Giulio Rossetti. Challenges in Community Discovery on Temporal Networks. Social Dimensions of Organised Crime. 2019; ():181-197.

Chicago/Turabian Style

Remy Cazabet; Giulio Rossetti. 2019. "Challenges in Community Discovery on Temporal Networks." Social Dimensions of Organised Crime , no. : 181-197.

Preprint
Published: 15 October 2019
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Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios

ACS Style

Salvatore Citraro; Giulio Rossetti. Eva: Attribute-Aware Network Segmentation. 2019, 1 .

AMA Style

Salvatore Citraro, Giulio Rossetti. Eva: Attribute-Aware Network Segmentation. . 2019; ():1.

Chicago/Turabian Style

Salvatore Citraro; Giulio Rossetti. 2019. "Eva: Attribute-Aware Network Segmentation." , no. : 1.

Research
Published: 29 July 2019 in Applied Network Science
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Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library - namely CDlib - designed to serve this need. The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.

ACS Style

Giulio Rossetti; Letizia Milli; Rémy Cazabet. CDLIB: a python library to extract, compare and evaluate communities from complex networks. Applied Network Science 2019, 4, 1 -26.

AMA Style

Giulio Rossetti, Letizia Milli, Rémy Cazabet. CDLIB: a python library to extract, compare and evaluate communities from complex networks. Applied Network Science. 2019; 4 (1):1-26.

Chicago/Turabian Style

Giulio Rossetti; Letizia Milli; Rémy Cazabet. 2019. "CDLIB: a python library to extract, compare and evaluate communities from complex networks." Applied Network Science 4, no. 1: 1-26.

Editorial
Published: 14 June 2019 in Online Social Networks and Media
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ACS Style

Rémy Cazabet; Andrea Passarella; Giulio Rossetti; Fabrizio Silvestri. Editorial - Special issue on OSNEM network properties and dynamics. Online Social Networks and Media 2019, 12, 21 .

AMA Style

Rémy Cazabet, Andrea Passarella, Giulio Rossetti, Fabrizio Silvestri. Editorial - Special issue on OSNEM network properties and dynamics. Online Social Networks and Media. 2019; 12 ():21.

Chicago/Turabian Style

Rémy Cazabet; Andrea Passarella; Giulio Rossetti; Fabrizio Silvestri. 2019. "Editorial - Special issue on OSNEM network properties and dynamics." Online Social Networks and Media 12, no. : 21.

Research
Published: 01 October 2018 in Applied Network Science
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Ideas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation. Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.

ACS Style

Letizia Milli; Giulio Rossetti; Dino Pedreschi; Fosca Giannotti. Active and passive diffusion processes in complex networks. Applied Network Science 2018, 3, 1 -15.

AMA Style

Letizia Milli, Giulio Rossetti, Dino Pedreschi, Fosca Giannotti. Active and passive diffusion processes in complex networks. Applied Network Science. 2018; 3 (1):1-15.

Chicago/Turabian Style

Letizia Milli; Giulio Rossetti; Dino Pedreschi; Fosca Giannotti. 2018. "Active and passive diffusion processes in complex networks." Applied Network Science 3, no. 1: 1-15.

Journal article
Published: 01 October 2018 in IEEE Transactions on Knowledge and Data Engineering
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Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity, and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.

ACS Style

Riccardo Guidotti; Giulio Rossetti; Luca Pappalardo; Fosca Giannotti; Dino Pedreschi. Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences. IEEE Transactions on Knowledge and Data Engineering 2018, 31, 2151 -2163.

AMA Style

Riccardo Guidotti, Giulio Rossetti, Luca Pappalardo, Fosca Giannotti, Dino Pedreschi. Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences. IEEE Transactions on Knowledge and Data Engineering. 2018; 31 (11):2151-2163.

Chicago/Turabian Style

Riccardo Guidotti; Giulio Rossetti; Luca Pappalardo; Fosca Giannotti; Dino Pedreschi. 2018. "Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences." IEEE Transactions on Knowledge and Data Engineering 31, no. 11: 2151-2163.

Article
Published: 17 September 2018 in Multimedia Tools and Applications
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Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.

ACS Style

Laura Pollacci; Riccardo Guidotti; Giulio Rossetti; Fosca Giannotti; Dino Pedreschi. The italian music superdiversity. Multimedia Tools and Applications 2018, 78, 3297 -3319.

AMA Style

Laura Pollacci, Riccardo Guidotti, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi. The italian music superdiversity. Multimedia Tools and Applications. 2018; 78 (3):3297-3319.

Chicago/Turabian Style

Laura Pollacci; Riccardo Guidotti; Giulio Rossetti; Fosca Giannotti; Dino Pedreschi. 2018. "The italian music superdiversity." Multimedia Tools and Applications 78, no. 3: 3297-3319.

Article
Published: 12 July 2018 in Journal of Grid Computing
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The community structure is one of the most studied features of the Online Social Networks (OSNs). Community detection guarantees several advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. Several challenges in DOSNs can be faced by exploiting communities. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic environments, where churn is a real problem. In this paper, we focus our attention on the analysis of dynamic community detection in DOSNs by studying a real Facebook dataset. We evaluate two different dynamic community discovery classes to understand which of them can be applied to a distributed environment. Results prove that the social graph has high instability and distributed solutions to manage the dynamism are needed and show that a Temporal Trade-off class is the most promising one.

ACS Style

Barbara Guidi; Andrea Michienzi; Giulio Rossetti. Towards the Dynamic Community Discovery in Decentralized Online Social Networks. Journal of Grid Computing 2018, 17, 23 -44.

AMA Style

Barbara Guidi, Andrea Michienzi, Giulio Rossetti. Towards the Dynamic Community Discovery in Decentralized Online Social Networks. Journal of Grid Computing. 2018; 17 (1):23-44.

Chicago/Turabian Style

Barbara Guidi; Andrea Michienzi; Giulio Rossetti. 2018. "Towards the Dynamic Community Discovery in Decentralized Online Social Networks." Journal of Grid Computing 17, no. 1: 23-44.

Journal article
Published: 02 June 2018 in ACM Computing Surveys
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Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.

ACS Style

Giulio Rossetti; Rémy Cazabet. Community Discovery in Dynamic Networks. ACM Computing Surveys 2018, 51, 1 -37.

AMA Style

Giulio Rossetti, Rémy Cazabet. Community Discovery in Dynamic Networks. ACM Computing Surveys. 2018; 51 (2):1-37.

Chicago/Turabian Style

Giulio Rossetti; Rémy Cazabet. 2018. "Community Discovery in Dynamic Networks." ACM Computing Surveys 51, no. 2: 1-37.

Conference paper
Published: 17 February 2018 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “fractal” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

ACS Style

Laura Pollacci; Riccardo Guidotti; Giulio Rossetti; Fosca Giannotti; Dino Pedreschi. The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018, 183 -194.

AMA Style

Laura Pollacci, Riccardo Guidotti, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi. The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2018; ():183-194.

Chicago/Turabian Style

Laura Pollacci; Riccardo Guidotti; Giulio Rossetti; Fosca Giannotti; Dino Pedreschi. 2018. "The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 183-194.

Conference paper
Published: 15 February 2018 in First Complex Systems Digital Campus World E-Conference 2015
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How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.

ACS Style

Lorenzo Gabrielli; Daniele Fadda; Giulio Rossetti; Mirco Nanni; Leonardo Piccinini; Dino Pedreschi; Fosca Giannotti; Patrizia Lattarulo. Discovering Mobility Functional Areas: A Mobility Data Analysis Approach. First Complex Systems Digital Campus World E-Conference 2015 2018, 311 -322.

AMA Style

Lorenzo Gabrielli, Daniele Fadda, Giulio Rossetti, Mirco Nanni, Leonardo Piccinini, Dino Pedreschi, Fosca Giannotti, Patrizia Lattarulo. Discovering Mobility Functional Areas: A Mobility Data Analysis Approach. First Complex Systems Digital Campus World E-Conference 2015. 2018; ():311-322.

Chicago/Turabian Style

Lorenzo Gabrielli; Daniele Fadda; Giulio Rossetti; Mirco Nanni; Leonardo Piccinini; Dino Pedreschi; Fosca Giannotti; Patrizia Lattarulo. 2018. "Discovering Mobility Functional Areas: A Mobility Data Analysis Approach." First Complex Systems Digital Campus World E-Conference 2015 , no. : 311-322.

Conference paper
Published: 15 February 2018 in First Complex Systems Digital Campus World E-Conference 2015
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Everyday, ideas, information as well as viruses spread over complex social tissues described by our interpersonal relations. So far, the network contexts upon which diffusive phenomena unfold have usually been considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work — following a data-driven approach — we compare the behaviors of classical spreading models when used to analyze a given social network whose topological dynamics are observed at different temporal granularities. Our goal is to shed some light on the impacts that the adoption of a static topology has on spreading simulations as well as to provide an alternative formulation of two classical diffusion models.

ACS Style

Letizia Milli; Giulio Rossetti; Dino Pedreschi; Fosca Giannotti. Diffusive Phenomena in Dynamic Networks: A Data-Driven Study. First Complex Systems Digital Campus World E-Conference 2015 2018, 151 -159.

AMA Style

Letizia Milli, Giulio Rossetti, Dino Pedreschi, Fosca Giannotti. Diffusive Phenomena in Dynamic Networks: A Data-Driven Study. First Complex Systems Digital Campus World E-Conference 2015. 2018; ():151-159.

Chicago/Turabian Style

Letizia Milli; Giulio Rossetti; Dino Pedreschi; Fosca Giannotti. 2018. "Diffusive Phenomena in Dynamic Networks: A Data-Driven Study." First Complex Systems Digital Campus World E-Conference 2015 , no. : 151-159.