activity
20112023
most citedTopology comparison of Twitter diffusion networks effectively reveals misleading information

85 citations · 215 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SI2023★ 4 cited

Metrics for network comparison using egonet feature distribution

Carlo Piccardi

Identifying networks with similar characteristics in a given ensemble, or detecting pattern discontinuities in a temporal sequence of networks, are two examples of tasks that requi…

cs.SI2020★ 3 cited

A multi-layer approach to disinformation detection on Twitter

Francesco Pierri, Carlo Piccardi, Stefano Ceri

We tackle the problem of classifying news articles pertaining to disinformation vs mainstream news by solely inspecting their diffusion mechanisms on Twitter. Our technique is inhe…

cs.SI2019★ 85 cited

Topology comparison of Twitter diffusion networks effectively reveals misleading information

Francesco Pierri, Carlo Piccardi, Stefano Ceri

In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analys…

nlin.AO2018★ 24 cited

Rethinking network reciprocity over social ties: local interactions make direct reciprocity possible and pave the rational way to cooperation

Fabio Dercole, Fabio Della Rossa, Carlo Piccardi

Since Nowak & May's (1992) influential paper, network reciprocity--the fact that individuals' interactions repeated within a local neighborhood support the evolution of cooperation…

physics.soc-ph2018★ 35 cited

Complexity, Centralization, and Fragility in Economic Networks

Carlo Piccardi, Lucia Tajoli

Trade networks, across which countries distribute their products, are crucial components of the globalized world economy. Their structure is strongly heterogeneous across products,…

physics.soc-ph2011★ 64 cited

Finding and testing network communities by lumped Markov chains

Carlo Piccardi

Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and func…