Social Network Analysis and Network Science

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The main research track of KDDLab in the field of Complex Networks is Multidimensional Network Analysis. Traditionally, Complex Network Analysis has been monodimensional: researchers focused their attention to network with a single kind of relation represented. KDDLab is pushing the research over multidimensional networks, i.e. network with multiple kind of relations, since they are a better model to represent the complexity in reality (transportation, infrastructure and social networks are often multidimensional). In this novel scenario, we want to develop a basic theory and simple analytical measures, as well as more complex analysis and algorithms, such as community discovery, link prediction and the analysis of highly connected nodes (i.e. hubs). From the main multidimensional network research, different branches have emerged: we are investigating also mobility networks, using human trajectories as edges to connect different geographical areas and/or points of interest. Also, we have interest in analyze trust networks, particularly the problem of identifying possible privacy and/or security fallacies: is it possible that following a path of "trusted" individuals the information can be lead to an individual not trusted by the source of the information?

Data Mining

Analysis methods and tools to extract knowledge hidden in the data, including frequent patterns, clustering and classification.

Data Visualization

Visual representation coupled with advanced analytics to comprehend and understand complex and large data.

Data Science

A combination of analytic, machine learning, data mining and statistical skills as well as experience with algorithms and technological tools.

Big Data

Acquiring strategies to manage and analyse large data sets and related tools such as MapReduce, Spark, Hive and Pigas well as NoSQL databases.

Community Analysis

Identify hidden sub-structures within complex networks and exploit them to bound homophilic behaviors.

Economic Complexity

Understand hidden features of products and customers studying their position in the network built over the market.

Network Dynamics

Track, understand and forecast topological perturbations that affect complex networks as time goes by.

Science of Success

Understanding the patterns of success in several fields: sports performance, popularity of artistic items, emergence of new technologies.

Sports Data Mining

Developing new methods of performance measurement by taking advantage of the huge growth of data collected during sport events.


Design algorithms for estimating the distribution of a population across different classes, and for tracking the changes in this distribution.

Spreading, Diffusion and Innovation

Design and develop useful tools for understanding, monitoring and signaling diffusion phenomena.

Well-Being Indicators

Developing of models to predict the well-being of territories based on Big Data on human behavior.

Discrimination Discovery and Prevention

Design algorithms for discovering discrimination in socially sensitive decision data and for enforcing fairness in data mining models.

Social Network Analysis

Extract meaningful knowledge from complex online and offline social contexts.

Multi-Dimensional Networks

Correlate multiple data sources to build and thus understand semantic enriched descriptions of real world networked contexts.