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2016
Conference article
Open Access
Going beyond GDP to nowcast Well-Being using retail market data
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
One of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.Source: NetSci-X 2016 - Advances in Network Science. 12th International Conference and School, pp. 29–42, Wroclaw, Poland, 11-13 January 2016
DOI: 10.1007/978-3-319-28361-6_3
Project(s): CIMPLEX
, SoBigData
Metrics:
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
One of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.Source: NetSci-X 2016 - Advances in Network Science. 12th International Conference and School, pp. 29–42, Wroclaw, Poland, 11-13 January 2016
DOI: 10.1007/978-3-319-28361-6_3
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
arpi.unipi.it 
| Lecture Notes in Computer Science 
| link.springer.com | CNR ExploRA
2015
Report
Unknown
Application of ETL techniques for SmartCity Malaga dataset
Rinzivillo S., Pennacchioli D., Gabrielli L., Giannotti F.
In this document we present a framework to aggregate data collected by sensors deployed in a portion of a distribution grid. The system provides functionalities to model the topological properties of the distribution grid, to harmonize and integrate readings coming from the sensors, to store and query efficiently the data, to visualize with a clear interface the timeseries collected. The rest of the document is organized as follows: first we show how we model the distribution grid with a graph-based representation; we describe the extraction, transformation and loading procedure; then we describe the visual interface to present the data to the analyst.Source: ISTI Technical reports, 2015
Rinzivillo S., Pennacchioli D., Gabrielli L., Giannotti F.
In this document we present a framework to aggregate data collected by sensors deployed in a portion of a distribution grid. The system provides functionalities to model the topological properties of the distribution grid, to harmonize and integrate readings coming from the sensors, to store and query efficiently the data, to visualize with a clear interface the timeseries collected. The rest of the document is organized as follows: first we show how we model the distribution grid with a graph-based representation; we describe the extraction, transformation and loading procedure; then we describe the visual interface to present the data to the analyst.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
Compatibility analysis of the current mobility with electrified vehicles (D4)
Rinzivillo S., Giannotti F., Pennacchioli D., Gabrielli L.
The availability of GPS-enabled devices has fostered the collection of large datasets of movements of people. This provides us a big opportunity to study human mobility behavior and to understand the key features to modify in order to improve the efficiency of individual movements. This efficiency has been studied in terms of mitigation of side effects of high density traffic, like jams, pollution, space occupancy. In this work, we concentrate on the study of the energy efficiency of movements, by considering a new emerging mean of transportation based on electric powered engines. In this document we explore the compatibility of the current mobility habits with electric engine technology, discussing improvement and solution to promote or improve the spatial range and extent of current vehicles.Source: ISTI Technical reports, 2015
Rinzivillo S., Giannotti F., Pennacchioli D., Gabrielli L.
The availability of GPS-enabled devices has fostered the collection of large datasets of movements of people. This provides us a big opportunity to study human mobility behavior and to understand the key features to modify in order to improve the efficiency of individual movements. This efficiency has been studied in terms of mitigation of side effects of high density traffic, like jams, pollution, space occupancy. In this work, we concentrate on the study of the energy efficiency of movements, by considering a new emerging mean of transportation based on electric powered engines. In this document we explore the compatibility of the current mobility habits with electric engine technology, discussing improvement and solution to promote or improve the spatial range and extent of current vehicles.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Conference article
Open Access
Behavioral entropy and profitability in retail
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer's behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.Source: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015
DOI: 10.1109/dsaa.2015.7344821
Project(s): CIMPLEX, SoBigData
Metrics:
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer's behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.Source: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015
DOI: 10.1109/dsaa.2015.7344821
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
www.michelecoscia.com | doi.org | ieeexplore.ieee.org | CNR ExploRA
2015
Conference article
Restricted
Interaction prediction in dynamic networks exploiting community discovery
Rossetti G., Guidotti R., Pennacchioli D., Pedreschi D., Giannotti F.
Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.Source: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 553–558, Paris, France, 25-28/08/2015
DOI: 10.1145/2808797.2809401
Project(s): CIMPLEX
Metrics:
Rossetti G., Guidotti R., Pennacchioli D., Pedreschi D., Giannotti F.
Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.Source: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 553–558, Paris, France, 25-28/08/2015
DOI: 10.1145/2808797.2809401
Project(s): CIMPLEX
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA
2015
Report
Unknown
RIS - Rapporto risultati sperimentazione
Pennè W., Gallo N., Nardini F. M., Pennacchioli D., Versienti L.
Il documento descrive gli scenari sperimentati per ogni componente del sistema: data mining, indicizzazione semantica, arricchimento del testo.Source: Project report, RIS, Deliverable D5.4.1, 2015
Pennè W., Gallo N., Nardini F. M., Pennacchioli D., Versienti L.
Il documento descrive gli scenari sperimentati per ogni componente del sistema: data mining, indicizzazione semantica, arricchimento del testo.Source: Project report, RIS, Deliverable D5.4.1, 2015
See at: CNR ExploRA
2015
Report
Restricted
RIS - Verifica del piano di test
Ghilardi M., Pennè W., Gallo N., Meghini C., Nardini F. M., Pennacchioli D., Versienti L.
Scopo di questo documento è illustrare il piano di test finale e le verifiche che saranno effettuate per il sistema nel suo complesso e per ogni sottosistema che lo compone.Source: Project report, RIS, Deliverable D5.2.5, 2015
Ghilardi M., Pennè W., Gallo N., Meghini C., Nardini F. M., Pennacchioli D., Versienti L.
Scopo di questo documento è illustrare il piano di test finale e le verifiche che saranno effettuate per il sistema nel suo complesso e per ogni sottosistema che lo compone.Source: Project report, RIS, Deliverable D5.2.5, 2015
See at:
puma.isti.cnr.it | CNR ExploRA
2015
Journal article
Open Access
Product assortment and customer mobility
Coscia M., Pennacchioli D., Giannotti F.
Customers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop's size and the customer's sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop's larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.Source: EPJ 4 (2015): 1–18. doi:10.1140/epjds/s13688-015-0051-3
DOI: 10.1140/epjds/s13688-015-0051-3
Metrics:
Coscia M., Pennacchioli D., Giannotti F.
Customers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop's size and the customer's sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop's larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.Source: EPJ 4 (2015): 1–18. doi:10.1140/epjds/s13688-015-0051-3
DOI: 10.1140/epjds/s13688-015-0051-3
Metrics:
See at:
EPJ Data Science | EPJ Data Science | link.springer.com | ISTI Repository | CNR ExploRA
2014
Conference article
Open Access
Overlap versus partition: marketing classification and customer profiling in complex networks ID products
Pennacchioli D., Coscia M., Pedreschi D.
In recent years we witnessed the explosion in the availability of data regarding human and customer behavior in the market. This data richness era has fostered the development of useful applications in understanding how markets and the minds of the customers work. In this paper we focus on the analysis of complex networks based on customer behavior. Complex network analysis has provided a new and wide toolbox for the classic data mining task of clustering. With community discovery, i.e. the detection of functional modules in complex networks, we are now able to group together customers and products using a variety of different criteria. The aim of this paper is to explore this new analytic degree of freedom. We are interested in providing a case study uncovering the meaning of different community discovery algorithms on a network of products connected together because co-purchased by the same customers. We focus our interest in the different interpretation of a partition approach, where each product belongs to a single community, against an overlapping approach, where each product can belong to multiple communities. We found that the former is useful to improve the marketing classification of products, while the latter is able to create a collection of different customer profiles.Source: ICDEW 2014 - 30th Data Engineering Workshops, in conjunction with ICDE 2014, pp. 103–110, Chicago, IL, USA, 31 March - 4 April 2014
DOI: 10.1109/icdew.2014.6818312
Metrics:
Pennacchioli D., Coscia M., Pedreschi D.
In recent years we witnessed the explosion in the availability of data regarding human and customer behavior in the market. This data richness era has fostered the development of useful applications in understanding how markets and the minds of the customers work. In this paper we focus on the analysis of complex networks based on customer behavior. Complex network analysis has provided a new and wide toolbox for the classic data mining task of clustering. With community discovery, i.e. the detection of functional modules in complex networks, we are now able to group together customers and products using a variety of different criteria. The aim of this paper is to explore this new analytic degree of freedom. We are interested in providing a case study uncovering the meaning of different community discovery algorithms on a network of products connected together because co-purchased by the same customers. We focus our interest in the different interpretation of a partition approach, where each product belongs to a single community, against an overlapping approach, where each product can belong to multiple communities. We found that the former is useful to improve the marketing classification of products, while the latter is able to create a collection of different customer profiles.Source: ICDEW 2014 - 30th Data Engineering Workshops, in conjunction with ICDE 2014, pp. 103–110, Chicago, IL, USA, 31 March - 4 April 2014
DOI: 10.1109/icdew.2014.6818312
Metrics:
See at:
www.michelecoscia.com | doi.org | CNR ExploRA
2014
Conference article
Unknown
The patterns of musical influence on the Last.Fm social network
Pennacchioli D., Rossetti G., Pappalardo L., Pedreschi D., Giannotti F., Coscia M.
One classic problem definition in social network analysis is the study of diffusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of influenced nodes, but this approach misses the fact that different scenarios imply different diffusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we study the patterns of musical influence through a social network. First, we define a procedure to extract musical leaders, i.e. users who start the diffusion of new music albums through the social network. Second, we measure three different dimensions of musical influence: the Width, i.e. the ratio of neighbors influenced by a leader; the Depth, i.e. the degrees of separation from a leader to its influenced nodes; and the Strength, i.e. the intensity of the influence from a leader. We validate our results on a social network extracted from the Last.Fm music platform.Source: 22nd Italian Symposium on Advanced Database Systems, pp. 284–291, Castellammare di Stabia, Italy, 16-18 June 2014
Project(s): DATA SIM
Pennacchioli D., Rossetti G., Pappalardo L., Pedreschi D., Giannotti F., Coscia M.
One classic problem definition in social network analysis is the study of diffusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of influenced nodes, but this approach misses the fact that different scenarios imply different diffusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we study the patterns of musical influence through a social network. First, we define a procedure to extract musical leaders, i.e. users who start the diffusion of new music albums through the social network. Second, we measure three different dimensions of musical influence: the Width, i.e. the ratio of neighbors influenced by a leader; the Depth, i.e. the degrees of separation from a leader to its influenced nodes; and the Strength, i.e. the intensity of the influence from a leader. We validate our results on a social network extracted from the Last.Fm music platform.Source: 22nd Italian Symposium on Advanced Database Systems, pp. 284–291, Castellammare di Stabia, Italy, 16-18 June 2014
Project(s): DATA SIM
See at: CNR ExploRA
2014
Journal article
Open Access
The retail market as a complex system
Pennacchioli D., Coscia M., Rinzivillo S., Giannotti F., Pedreschi D.
Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country's GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products' volumes of sales with the customers' volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.Source: EPJ 3 (2014). doi:10.1140/epjds/s13688-014-0033-x
DOI: 10.1140/epjds/s13688-014-0033-x
Project(s): DATA SIM
Metrics:
Pennacchioli D., Coscia M., Rinzivillo S., Giannotti F., Pedreschi D.
Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country's GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products' volumes of sales with the customers' volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.Source: EPJ 3 (2014). doi:10.1140/epjds/s13688-014-0033-x
DOI: 10.1140/epjds/s13688-014-0033-x
Project(s): DATA SIM
Metrics:
See at:
EPJ Data Science | EPJ Data Science | www.epjdatascience.com | CNR ExploRA
2014
Report
Unknown
RIS - Architettura del sistema RIS (Architettura Finale)
Gallo N., Meghini C., Nardini F. M., Pennacchioli D., Sartiano D., Versienti L.
This document presents the RIS final architecture and the Use cases developed in realtion to this architecture.Source: Project report, RIS, Deliverable D1.4.2, 2014
Gallo N., Meghini C., Nardini F. M., Pennacchioli D., Sartiano D., Versienti L.
This document presents the RIS final architecture and the Use cases developed in realtion to this architecture.Source: Project report, RIS, Deliverable D1.4.2, 2014
See at: CNR ExploRA
2013
Report
Restricted
"You Know Because I Know": a multidimensional network approach to human resources problem
Coscia M., Rossetti G., Pennacchioli D., Ceccarelli D., Giannotti F.
Finding talents, often among the people already hired, is an endemic challenge for organizations. The social networking revolution, with online tools like Linkedin, made possible to make explicit and accessible what we perceived, but not used, for thousands of years: the exact position and ranking of a person in a network of professional and personal connections. To search and mine where and how an employee is positioned on a global skill network will enable organizations to find unpredictable sources of knowledge, innovation and know-how. This data richness and hidden knowledge demands for a multidimensional and multiskill approach to the network ranking problem. Multidimensional networks are networks with multiple kinds of relations. To the best of our knowledge, no network-based ranking algorithm is able to handle multidimensional networks and multiple rankings over multiple attributes at the same time. In this paper we propose such an algorithm, whose aim is to address the node multi-ranking problem in multidimensional networks. We test our algorithm over several real world networks, extracted from DBLP and the Enron email corpus, and we show its usefulness in providing less trivial and more flexible rankings than the current state of the art algorithms.Source: ISTI Technical reports, 2013
Project(s): DATA SIM
Coscia M., Rossetti G., Pennacchioli D., Ceccarelli D., Giannotti F.
Finding talents, often among the people already hired, is an endemic challenge for organizations. The social networking revolution, with online tools like Linkedin, made possible to make explicit and accessible what we perceived, but not used, for thousands of years: the exact position and ranking of a person in a network of professional and personal connections. To search and mine where and how an employee is positioned on a global skill network will enable organizations to find unpredictable sources of knowledge, innovation and know-how. This data richness and hidden knowledge demands for a multidimensional and multiskill approach to the network ranking problem. Multidimensional networks are networks with multiple kinds of relations. To the best of our knowledge, no network-based ranking algorithm is able to handle multidimensional networks and multiple rankings over multiple attributes at the same time. In this paper we propose such an algorithm, whose aim is to address the node multi-ranking problem in multidimensional networks. We test our algorithm over several real world networks, extracted from DBLP and the Enron email corpus, and we show its usefulness in providing less trivial and more flexible rankings than the current state of the art algorithms.Source: ISTI Technical reports, 2013
Project(s): DATA SIM
See at:
arxiv.org | CNR ExploRA
2013
Conference article
Open Access
The three dimensions of social prominence
Pennacchioli D., Rossetti G., Pappalardo L., Pedreschi D., Giannotti F., Coscia M.
One classic problem denition in social network analysis is the study of diusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of in uenced nodes, but this approach misses the fact that dierent scenarios imply dierent dif- fusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we measure three dierent dimensions of social prominence: the Width, i.e. the ratio of neighbors in uenced by a node; the Depth, i.e. the degrees of separation from a node to the nodes perceiv- ing its prominence; and the Strength, i.e. the intensity of the prominence of a node. By dening a procedure to extract prominent users in complex networks, we detect associations between the three dimensions of social prominence and classical network statistics. We validate our results on a social network extracted from the Last.Fm music platform.Source: SocInfo2013 - Social Informatics. 5th International Conference, pp. 319–332, Kyoto, Japan, 25-27 November 2013
DOI: 10.1007/978-3-319-03260-3_28
Project(s): DATA SIM
Metrics:
Pennacchioli D., Rossetti G., Pappalardo L., Pedreschi D., Giannotti F., Coscia M.
One classic problem denition in social network analysis is the study of diusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of in uenced nodes, but this approach misses the fact that dierent scenarios imply dierent dif- fusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we measure three dierent dimensions of social prominence: the Width, i.e. the ratio of neighbors in uenced by a node; the Depth, i.e. the degrees of separation from a node to the nodes perceiv- ing its prominence; and the Strength, i.e. the intensity of the prominence of a node. By dening a procedure to extract prominent users in complex networks, we detect associations between the three dimensions of social prominence and classical network statistics. We validate our results on a social network extracted from the Last.Fm music platform.Source: SocInfo2013 - Social Informatics. 5th International Conference, pp. 319–332, Kyoto, Japan, 25-27 November 2013
DOI: 10.1007/978-3-319-03260-3_28
Project(s): DATA SIM
Metrics:
See at:
www.michelecoscia.com | doi.org | link.springer.com | CNR ExploRA
2013
Conference article
Restricted
Explaining the product range effect in purchase data
Pennacchioli D., Coscia M., Pedreschi D., Giannotti F.
In our market society, buyers are considered rational entities, driven by two utility functions: i) the amount of money spent, a universal quantity to be minimized; and ii) the individual needs to satisfy, a personal quantity, varying from person to person, to be maximized. In this paper, we propose an analytic framework based on big data to measure the personal utility function and we prove that this function has a stronger effect on customer behavior than the price. By focusing on the purchases in an Italian supermarket chain, we discover and describe a range effect of products: the more sophisticated the needs they satisfy, the more cost the customers are willing to pay to buy them, in terms of distance to travel more than in terms of the price of the item itself. We exhibit a striking empirical evidence of this theory by tracking the geographical information about points of sale and customers, in a large dataset containing tens of thousands of customers and thousands of products. We create a data mining framework able to scale to possibly hundreds of thousands, or millions, of customers and to let emerge from the data the knowledge about the actual range of each product. As an application of this finding, we show how it is possible to accurately predict how long a customer will travel (or which shop she will choose) to buy a product, as a function of the product's sophistication.Source: 2013 IEEE International Conference on Big Data, pp. 648–656, Santa Clara, CA, USA, 6-9 October 2013
Project(s): DATA SIM
Pennacchioli D., Coscia M., Pedreschi D., Giannotti F.
In our market society, buyers are considered rational entities, driven by two utility functions: i) the amount of money spent, a universal quantity to be minimized; and ii) the individual needs to satisfy, a personal quantity, varying from person to person, to be maximized. In this paper, we propose an analytic framework based on big data to measure the personal utility function and we prove that this function has a stronger effect on customer behavior than the price. By focusing on the purchases in an Italian supermarket chain, we discover and describe a range effect of products: the more sophisticated the needs they satisfy, the more cost the customers are willing to pay to buy them, in terms of distance to travel more than in terms of the price of the item itself. We exhibit a striking empirical evidence of this theory by tracking the geographical information about points of sale and customers, in a large dataset containing tens of thousands of customers and thousands of products. We create a data mining framework able to scale to possibly hundreds of thousands, or millions, of customers and to let emerge from the data the knowledge about the actual range of each product. As an application of this finding, we show how it is possible to accurately predict how long a customer will travel (or which shop she will choose) to buy a product, as a function of the product's sophistication.Source: 2013 IEEE International Conference on Big Data, pp. 648–656, Santa Clara, CA, USA, 6-9 October 2013
Project(s): DATA SIM
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ieeexplore.ieee.org | CNR ExploRA
2013
Conference article
Open Access
"You know because I know": a multidimensional network approach to human resources problem
Coscia M., Rossetti G., Pennachioli D., Ceccarelli D., Giannotti F.
Finding talents, often among the people already hired, is an endemic challenge for organizations. The social networking revolution, with online tools like Linkedin, made possible to make explicit and accessible what we perceived, but not used, for thousands of years: the exact position and ranking of a person in a network of professional and personal connections. To search and mine where and how an employee is positioned on a global skill network will enable organizations to find unpredictable sources of knowledge, innovation and know- how. This data richness and hidden knowledge demands for a multidimensional and multiskill approach to the network ranking problem. Multidimensional networks are networks with multiple kinds of relations. To the best of our knowledge, no network-based ranking algorithm is able to handle multidimensional networks and multiple rankings over multiple attributes at the same time. In this paper we propose such an algorithm, whose aim is to address the node multi-ranking problem in multidimensional networks. We test our algorithm over several real world networks, extracted from DBLP and the Enron email corpus, and we show its usefulness in providing less trivial and more flexible rankings than the current state of the art algorithms.Source: ASONAM - 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 434–441, Niagara Falls, Canada, 25-28 August 2013
DOI: 10.1145/2492517.2492537
DOI: 10.48550/arxiv.1305.7146
Project(s): DATA SIM
Metrics:
Coscia M., Rossetti G., Pennachioli D., Ceccarelli D., Giannotti F.
Finding talents, often among the people already hired, is an endemic challenge for organizations. The social networking revolution, with online tools like Linkedin, made possible to make explicit and accessible what we perceived, but not used, for thousands of years: the exact position and ranking of a person in a network of professional and personal connections. To search and mine where and how an employee is positioned on a global skill network will enable organizations to find unpredictable sources of knowledge, innovation and know- how. This data richness and hidden knowledge demands for a multidimensional and multiskill approach to the network ranking problem. Multidimensional networks are networks with multiple kinds of relations. To the best of our knowledge, no network-based ranking algorithm is able to handle multidimensional networks and multiple rankings over multiple attributes at the same time. In this paper we propose such an algorithm, whose aim is to address the node multi-ranking problem in multidimensional networks. We test our algorithm over several real world networks, extracted from DBLP and the Enron email corpus, and we show its usefulness in providing less trivial and more flexible rankings than the current state of the art algorithms.Source: ASONAM - 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 434–441, Niagara Falls, Canada, 25-28 August 2013
DOI: 10.1145/2492517.2492537
DOI: 10.48550/arxiv.1305.7146
Project(s): DATA SIM
Metrics:
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arXiv.org e-Print Archive | dl.acm.org | doi.org | doi.org | CNR ExploRA