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2021 Conference article Open Access OPEN

Measuring immigrants adoption of natives shopping consumption with machine learning
Guidotti R., Nanni M., Giannotti F., Pedreschi D., Bertoli S., Speciale B., Rapoport H.
Tell me what you eat and I will tell you what you are". Jean Anthelme Brillat-Savarin was among the firsts to recognize the relationship between identity and food consumption. Food adoption choices are much less exposed to external judgment and social pressure than other individual behaviours, and can be observed over a long period. That makes them an interesting basis for, among other applications, studying the integration of immigrants from a food consumption viewpoint. Indeed, in this work we analyze immigrants' food consumption from shopping retail data for understanding if and how it converges towards those of natives. As core contribution of our proposal, we define a score of adoption of natives' consumption habits by an individual as the probability of being recognized as a native from a machine learning classifier, thus adopting a completely data-driven approach. We measure the immigrant's adoption of natives' consumption behavior over a long time, and we identify different trends. A case study on real data of a large nation-wide supermarket chain reveals that we can distinguish five main different groups of immigrants depending on their trends of native consumption adoption.Source: ECML PKDD 2020 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 369–385, Ghent, Belgium, September 14-18, 2020
DOI: 10.1007/978-3-030-67670-4_23

See at: ISTI Repository Open Access | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted


2021 Report Open Access OPEN

Improving vehicles' emissions reduction policies by targeting gross polluters
Böhm M., Nanni M., Pappalardo L.
Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Conference article Open Access OPEN

Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution
Böhm M., Nanni M., Pappalardo L.
Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities.Source: NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Online conference, 11/12/2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.climatechange.ai Open Access


2020 Report Open Access OPEN

Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Bonato P., Cintia P., Fabbri F., Fadda D., Giannotti F., Lopalco P. L., Mazzilli S., Nanni M., Pappalardo L., Pedreschi D., Penone F., Rinzivillo S., Rossetti G., Savarese M., Tavoschi L.
Understanding human mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in "stand-by" to fight the diffusion of the epidemics. A recent analysis shows that, following the national lockdown of March 9th, the mobility fluxes have decreased by 50% or more, everywhere in the country. To this purpose, we use mobile phone data to compute the movements of people between Italian provinces, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modeling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. In this report, we address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?Source: ISTI Technical Reports 005/2020, 2020, 2020
DOI: 10.32079/isti-tr-2020/005

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Journal article Open Access OPEN

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M., Andrienko G., Barabasi A. -l., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comandé G., Conti M., Coté M., Dignum F., Dignum V., Domingo-ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Van Den Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Transactions on data privacy 13 (2020): 61–66.

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.tdp.cat Open Access


2020 Conference article Open Access OPEN

Self-Adapting Trajectory Segmentation
Bonavita A., Guidotti R., Nanni M.
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, 30/03/2020
Project(s): Track and Know via OpenAIRE

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Conference article Open Access OPEN

Data-Driven Location Annotation for Fleet Mobility Modeling
Guidotti R., Nanni M., Sbolgi F.
The large availability of mobility data allows studying human behavior and human activities. However, this massive and raw amount of data generally lacks any detailed semantics or useful categorization. Annotations of the locations where the users stop may be helpful in a number of contexts, including user modeling and profiling, urban planning, activity recommendations, and can even lead to a deeper understanding of the mobility evolution of an urban area. In this paper, we foster the expressive power of individual mobility networks, a data model describing users' behavior, by defining a data-driven procedure for locations annotation. The procedure considers individual, collective, and contextual features for turning locations into annotated ones. The annotated locations own a high expressiveness that allows generalizing individual mobility networks, and that makes them comparable across different users. The results of our study on a dataset of trucks moving in Greece show that the annotated individual mobility networks can enable detailed analysis of urban areas and the planning of advanced mobility applications.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, 30/03/2020
Project(s): Track and Know via OpenAIRE

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Master thesis Unknown

Generative Models of Human Mobility based on Deep Learning
Briganti S.
Goal of the thesis is the generation of synthetic human mobility based on Deep Learning. Three different generative recurrent models have been implemented: a Seq2Seq Variational Autoencoder (VAE), a Generative Adversarial Network (GAN) and a Wasserstein GAN. The aim of this study is the generation of a synthetic dataset of GPS trajectories having characteristics and typical measures proper of the real human mobility. Scopo della tesi è la generazione di mobilità umana sintetica basata suDeep Learning. Sono stati implementati tre modelli generativi: un Seq2Seq Variational Autoencoder (VAE), una Generative Adversarial Network (GAN) e una Wasserstein GAN. Obiettivo finale dello studio è lagenerazione di un dataset sintetico di traiettorie GPS, avente caratteristiche e misure proprie della mobilità umana.Project(s): SoBigData via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA


2020 Journal article Open Access OPEN

Ranking places in attributed temporal urban mobility networks
Nanni M., Tortosa L., Vicent J. F., Yeghikyan G.
Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots"of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots"and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.Source: PloS one 15 (2020). doi:10.1371/journal.pone.0239319
DOI: 10.1371/journal.pone.0239319
Project(s): Track and Know via OpenAIRE

See at: PLoS ONE Open Access | PLoS ONE Open Access | PLoS ONE Open Access | Europe PubMed Central Open Access | PLoS ONE Open Access | PLoS ONE Open Access | journals.plos.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | PLoS ONE Open Access | PLoS ONE Open Access | PLoS ONE Open Access


2020 Conference article Open Access OPEN

Towards in-memory sub-trajectory similarity search
Alamdari I., Nanni M., Trasarti R., Pedreschi D.
Spatial-temporal trajectory data contains rich information aboutmoving objects and has been widely used for a large numberof real-world applications. However, the complexity of spatial-temporal trajectory data, on the one hand, and the fast collectionof datasets, on the other hand, has made it challenging to ef-ficiently store, process, and query such data. In this paper, wepropose a scalable method to analyze the sub-trajectory simi-larity search in an in-memory cluster computing environment.Notably, we have extended Apache Spark with efficient trajectoryindexing, partitioning, and querying functionalities to supportthe sub-trajectory similarity query. Our experiments on a realtrajectory dataset have shown the efficiency and effectiveness ofthe proposed method.Source: EDBT/ICDT 2020 Joint Conference - International Workshop in Big Mobility Data Analytics, Copenhagen, Denmark, 30th March - 2nd April, 2020
Project(s): Track and Know via OpenAIRE

See at: ceur-ws.org Open Access | CNR ExploRA Open Access


2020 Conference article Open Access OPEN

Discovering tourist attractions of cities using Flickr and OpenStreetMap Data
Vaziri F., Nanni M., Matwin S., Pedreschi D.
Tourism is a growing industry which needs accurate management and planning. Photography and tourism are inseparable; Photographs play the role of tourists' footprints during their visit to a touristic city. Nowadays, the large deployment of mobile devices and digital cameras has led to a massive increase in the volume of records of where people have been and when they were there. In this paper, we introduce a new method to automatically discover the touristic attractions of every single city with the use of two open-source platforms, Flickr and OpenStreetMap. We applied techniques to convert raw metadata of geotagged photos downloaded from Flickr to information about popular Points of Interest with the help of additional information retrieved from OpenStreetMap.Source: Advances in Tourism, Technology and Smart Systems, pp. 231–241, 5/12/2019
DOI: 10.1007/978-981-15-2024-2_21
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | Archivio della Ricerca - Università di Pisa Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted


2020 Conference article Open Access OPEN

Learning mobility flows from urban features with spatial interaction models and neural networks
Yeghikyan G., Opolka F. L., Nanni M., Lepri B., Lio P.
A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be modelled as attributed graphs with both node and edge features characterising locations in a city and the various types of relationships between them. In this paper, we address the problem of assessing origin-destination (OD) car flows between a location of interest and every other location in a city, given their features and the structural characteristics of the graph. We propose three neural network architectures, including graph neural networks (GNN), and conduct a systematic comparison between the proposed methods and state-of-the-art spatial interaction models, their modifications, and machine learning approaches. The objective of the paper is to address the practical problem of estimating potential flow between an urban project location and other locations in the city, where the features of the project location are known in advance. We evaluate the performance of the models on a regression task using a custom data set of attributed car OD flows in London. We also visualise the model performance by showing the spatial distribution of flow residuals across London.Source: SMARTCOMP 2020 - IEEE International Conference on Smart Computing, pp. 57–64, Bologna, Italy, September 14-17, 2020
DOI: 10.1109/smartcomp50058.2020.00028
Project(s): Track and Know via OpenAIRE

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | cris.fbk.eu Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2020 Master thesis Unknown

Machine Learning-based estimation of electrical vehicle battery consumption over road networks
Babazadeh M.
In this Thesis, the author tried to analyze the GPS dataset of cars that traverse a variety of roads in a different time to make an estimation to calculate the battery consumption for the new generation of cars(EVs). The thesis investigates different ways to find a algorithm to make a fast calculation as much as possible.Project(s): Track and Know via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA


2020 Master thesis Unknown

Simulating individual mobility network for electric vehicles
Shajari S.
Electric mobility appears to be one of the future ways to make cities more sustainable and improve the quality of life in urban environments. However, when it comes to private vehicles, users need to evaluate how their mobility lifestyle is going to change when their fuel-based vehicle is replaced by and electric one (EV). The objective of this work is to propose a process that, through a mix of mobility data analytics, ad hoc trip planning and simulation, is able to analyze the current fuel-based mobility of a user and quantitatively describe the impact of switching to EVs on her mobility life style. Exploiting a network- based representation of human mobility (Individual Mobility Networks), four simulation scenarios are considered, distinguished by the battery recharge options that the user might have in real life: recharging only at public stations, charging also at home, or also at work, or both. For each scenario we calculate how much battery the user has to charge in each charging option and how much time he has to wait for charging, as well as how much her original mobility (performed with a combustion engine) is affected by the limits of EVs, evaluating the expected increment in travel times and distances. This work is part of the activities of the H2020 European Project Track Know (https://trackandknowproject.eu/).Project(s): Track and Know via OpenAIRE

See at: CNR ExploRA


2020 Master thesis Unknown

Fostering the expressive power of individual mobility networks for fleet trajectories modeling
Sbolgi F.
The quick evolution and wide diffusion of technologies for the localization of devices, especially smartphones and vehicles' GPS, is leading to the production and collection of large and diversified traces of human mobility. This large availability of mobility data allows us to investigate complex phenomena about human movement and to study the human behavior. However this abundance of raw data usually comes with few additional information about the points collected. Hence, in order to unlock this potential, we need to define methods for processing and analyzing mobility data. In this thesis we foster the expressive power of Individual Mobility Networks (IMNs), a data model describing a user mobility, to create a procedure to annotate the locations where the users have stopped. We have called the combination of IMNs with these labels Annotated IMNs (AIMNs). They allow a generalization which makes the locations and the vehicles comparable. The procedure exploits a set of features based on different characteristics of a location. Then, by applying a clustering process, obtains a small set of labels that can be used to classify the vehicles according to the type of locations they visit. We tested the algorithm on a dataset of trucks moving in Greece. The results show that the AIMNs can enable detailed analysis of urban areas and the planning for advanced mobility applications.Project(s): Track and Know via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA


2020 Master thesis Unknown

Graph pattern-based analysis of call detail records data for dynamic population estimation
Osmani N.
The prevalent use of mobile phones by individuals generate an immense amount of information about the mobility of people for network operators. This data which is called Call Detail Records can be used to study the movement behaviors of individuals, the collective behavior of the population in different circumstances such as big events, and evacuation. Policy-makers can utilize this data for planning purposes. One of the main problems with mobile phone data is their sparsity and low position accuracy. In this thesis, we analyzed different sources of mobile phone positioning errors and removed the noises for subsequent analysis purposes. We then obtained the stay locations from the trajectories of users and used them as an indicator of the population in different spatial areas and temporal intervals. We also used this data to generate mobility networks for each user. We augmented our research with a visualization dashboard that is suitable to analyze the mobile phone data, allowing us to apply different filters on different dimensions.Project(s): Track and Know via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA


2020 Doctoral thesis Open Access OPEN

Urban structure and mobility as spatio-temporal complex networks
Yeghikyan G.
Contemporary urban life and functioning have become increasingly dependent on mobility. Having become an inherent constituent of urban dynamics, the role of urban mobility in influencing urban processes and morphology has increased dramatically. However, the relationship between urban mobility and spatial socio-economic structure has still not been thoroughly understood. This work will attempt to take a complex network theoretical approach to study this intricate relationship through o the Spatio-temporal evolution of ad-hoc developed network centralities based on the Google PageRank, o multilayer network regression with statistical random graphs respecting network structures for explaining urban mobility flows from urban socio-economic attributes, o and Graph Neural Networks for predicting mobility flows to or from a specific location in the city. Making both practical and theoretical contributions to urban science by offering methods for describing, monitoring, explaining, and predicting urban dynamics, this work will thus be aimed at providing a network theoretical framework for developing tools to facilitate better decision-making in urban planning and policymaking.Project(s): Track and Know via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2019 Journal article Open Access OPEN

Car telematics big data analytics for insurance and innovative mobility services
Longhi L., Nanni M.
Car telematics is a large and growing business sector aiming to collect mobility-related data (mainly private and commercial vehicles) and to develop services of various nature both for individual citizens and other companies. Such services and applications include information systems to support car insurances, info-mobility services, ad hoc studies for planning purposes, etc. In this work we report and discuss some of the key challenges that a car telematics pilot application is facing within the EU project "Track and Know". The paper introduces the overall context, the main business goals identified as potentially beneficial of big data solutions and the type of data sources that such applications can rely on (in particular, those available within the project for experimental studies), then discusses initial results of the solutions developed so far and ongoing lines of research. In particular, the discussion will focus on the most relevant applications identified for the project purposes, namely new services for car insurance, electric vehicles mobility and car- and ride-sharing.Source: Journal of ambient intelligence & humanized computing (Print) 11 (2019): 3989–3999. doi:10.1007/s12652-019-01632-4
DOI: 10.1007/s12652-019-01632-4
Project(s): Track and Know via OpenAIRE

See at: ISTI Repository Open Access | Journal of Ambient Intelligence and Humanized Computing Restricted | Journal of Ambient Intelligence and Humanized Computing Restricted | Journal of Ambient Intelligence and Humanized Computing Restricted | Journal of Ambient Intelligence and Humanized Computing Restricted | Journal of Ambient Intelligence and Humanized Computing Restricted | CNR ExploRA Restricted


2018 Contribution to book Open Access OPEN

How data mining and machine learning evolved from relational data base to data science
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17

See at: Archivio della Ricerca - Università di Pisa Open Access | ISTI Repository Open Access | academic.microsoft.com Restricted | arpi.unipi.it Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


2018 Conference article Open Access OPEN

Discovering Mobility Functional Areas: A Mobility Data Analysis Approach
Gabrielli L., Fadda D., Rossetti G., Nanni M., Piccinini L., Pedreschi D., Giannotti F., Lattarulo P.
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.Source: 9th Conference on Complex Networks, CompleNet, pp. 311–322, Boston, 6/03/2018
DOI: 10.1007/978-3-319-73198-8_27
Project(s): SoBigData via OpenAIRE

See at: ISTI Repository Open Access | ISTI Repository Open Access | academic.microsoft.com Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted