activity
20122023
most citedA Classification for Community Discovery Methods in Complex Networks

297 citations · 368 across the 11 of their papers we have counts for

collaborators

13 papers

physics.soc-ph2023

Displacement and disconnection: the impact of violence on migration networks and highway traffic in Mexico

Michele Coscia, Roxana Gutiérrez-Romero

We examine how violence affects migration flows and, crucially, how it reshapes the strength of migration networks -- measured by the intensity of migration between areas, accounti…

cs.SI2022

Fast Multiplex Graph Association Rules for Link Prediction

Michele Coscia, Christian Borgelt, Michael Szell

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social…

cs.CY2021★ 3 cited

The Atlas for the Aspiring Network Scientist

Michele Coscia

Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks. We normally model such networks as graphs: sets o…

cs.SI2020★ 3 cited

Multiplex Graph Association Rules for Link Prediction

Michele Coscia, Michael Szell

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social…

cs.SI2019

Discovering Communities of Community Discovery

Michele Coscia

Discovering communities in complex networks means grouping nodes similar to each other, to uncover latent information about them. There are hundreds of different algorithms to solv…

cs.SI2019

The Impact of Projection and Backboning on Network Topologies

Michele Coscia, Luca Rossi

Bipartite networks are a well known strategy to study a variety of phenomena. The commonly used method to deal with this type of network is to project the bipartite data into a uni…

physics.soc-ph2017★ 2 cited

Network Backboning with Noisy Data

Michele Coscia, Frank Neffke

Networks are powerful instruments to study complex phenomena, but they become hard to analyze in data that contain noise. Network backbones provide a tool to extract the latent str…

q-fin.GN2016★ 1 cited

Exploring the Uncharted Export: an Analysis of Tourism-Related Foreign Expenditure with International Spend Data

Michele Coscia, Ricardo Hausmann, Frank Neffke

Tourism is one of the most important economic activities in the world: for many countries it represents the single largest product in their export basket. However, it is a product…

cs.SI2016

Report on the Poblacion Flotante of Bogota (D.C.)

Michele Coscia, Frank Neffke, Eduardo Lora

In this document we describe the size of the Poblacion Flotante of Bogota (D.C.). The Poblacion Flotante is composed by people who live outside Bogota (D.C.), but who rely on the c…

cs.SI2013★ 13 cited

"You Know Because I Know": a Multidimensional Network Approach to Human Resources Problem

Michele Coscia, Giulio Rossetti, Diego Pennacchioli +2

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 t…

physics.soc-ph2013★ 8 cited

Competition and Success in the Meme Pool: a Case Study on Quickmeme.com

Michele Coscia

The advent of social media has provided data and insights about how people relate to information and culture. While information is composed by bits and its fundamental building bri…

cs.SI2012★ 297 cited

A Classification for Community Discovery Methods in Complex Networks

Michele Coscia, Fosca Giannotti, Dino Pedreschi

In the last few years many real-world networks have been found to show a so-called community structure organization. Much effort has been devoted in the literature to develop metho…

cs.DS2012★ 41 cited

DEMON: a Local-First Discovery Method for Overlapping Communities

Michele Coscia, Giulio Rossetti, Fosca Giannotti +1

Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. How…