most citedDrivers of the decrease of patent similarities from 1976 to 2021

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2023

Modeling non-linear Effects with Neural Networks in Relational Event Models

Edoardo Filippi-Mazzola, Ernst C. Wit

Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, es…

stat.AP2023

Relational Event Modeling

Federica Bianchi, Edoardo Filippi-Mazzola, Alessandro Lomi +1

Advances in information technology have increased the availability of time-stamped relational data such as those produced by email exchanges or interaction through social media. Wh…

stat.CO2023★ 2 cited

A Stochastic Gradient Relational Event Additive Model for modelling US patent citations from 1976 until 2022

Edoardo Filippi-Mazzola, Ernst C. Wit

Until 2022, the US patent citation network contained almost 10 million patents and over 100 million citations. To overcome limitations in analyzing such complex networks, we propos…

stat.AP2022★ 3 cited

Drivers of the decrease of patent similarities from 1976 to 2021

Edoardo Filippi-Mazzola, Federica Bianchi, Ernst C. Wit

The citation network of patents citing prior art arises from the legal obligation of patent applicants to properly disclose their invention. One way to study the relationship betwe…

stat.ME2022

Model-based clustering of categorical data based on the Hamming distance

Raffaele Argiento, Edoardo Filippi-Mazzola, Lucia Paci

A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability ma…