Giacomo Vaccario

gvaccario@ethz.ch

+41 44 632 82 11

ETH Zurich
Giacomo Vaccario
Chair of Systems Design
WEV G 206
Weinbergstrasse 56/58
8092 Zurich

My main interests are in the quantification of knowledge and of its exchange in academia and in R&D activities.

In particular, I concentrate on two specific classes of problems. The first class of problems is related to the question of how knowledge artifacts are linked to each other, ranked and filtered in repositories. Examples of repositories are patent and scientific publication databases. The second class of problems relates to the question of how knowledge is exchanged and transferred. Indeed, knowledge is not only produced and encoded in knowledge artifacts, as patents or scientific publications, but it is also exchanged by humans and it diffuses in our society. R&D alliances among firms and co-authorship of papers among scientists are examples of activities favoring knowledge exchange. While the physical migration of inventors or scientists is an example of knowledge transfer/migration.

To answer the above questions, I develop statistical methods and agent-based models.

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Publications»

Publications in

Quantifying knowledge exchange in R&D networks: A data-driven model

[2018]
Vaccario, Giacomo; Tomasello, Mario Vincenzo; Tessone, Claudio Juan; Schweitzer, Frank

Journal of Evolutionary Economics, pages: 461-493, volume: 28, number: 3

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Data-driven modeling of collaboration networks: A cross-domain analysis

[2017]
Tomasello, Mario Vincenzo; Vaccario, Giacomo; Schweitzer, Frank

EPJ Data Sci., pages: 22, volume: 6

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Quantifying and suppressing ranking bias in a large citation network

[2017]
Vaccario, Giacomo; Medo, Matus; Wider, Nicolas; Mariani, Manuel S.

Journal of Informetrics, pages: 766-782, volume: 11, number: 3

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Talks»

Talks

Quantifying and suppressing ranking bias [March 11, 2018 - March 16, 2018]

DPG-Frühjahrstagung, Berlin, Germany

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How do firms collaborate? A data-driven model [March 11, 2018 - Feb. 16, 2018]

DPG-Frühjahrstagung, Berlin, Germany

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Quantifying knowledge exchange in R&D networks [Sept. 21, 2017]

KU Leuven - Summer School on Data & Algorithms for STI studies

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Quantifying knowledge exchange in R&D networks: A data-driven model [Sept. 7, 2017]

Kreyon 2017 - Roma

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Collaborations Across Economic and Scientific Domains [Sept. 19, 2016]

Conference on Complex Systems - Amsterdam

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Quantifying and suppressing ranking bias»

Every day scholars and online users explore available knowledge using recommender systems based on ranking algorithms. This challenge us to design more sophisticated filtering and ranking procedures to avoid biases that can systematically hide relevant contents.

In this work, we tackle this issue by quantifying and suppressing biases of indicators of scientific impact. We use a large citation dataset from Microsoft Academic Graph and a new statistical framework based on the Mahalanobis distance to show that the rankings by well known indicators, including relative citation count and Google's PageRank score, are significantly biased by paper field and age. We propose a general normalization procedure motivated by the z-score which produces much less biased rankings when applied to citation count and PageRank score.

 

We provide a simple and quick tutorial on how we quantify ranking bias. Source codes and tutotial can be found in this gitHub project. Enjoy!

Research Plan»

For the PhD, I focus on the organization, exchange and transfer ot knowledge is socio-technical systems.

My full research plan can be found here and in the follwoing I summarie the 7 research questions (RQs) that I will answer in PhD thesis. These 7 questions can be divided in three different sections.

The organization of Knowledge

RQ1: Assessing multiple normalizations. We need to age- and field- normalize citation-based indicators in order to compare documents of different age and from different fields. How can we assess that these indicators have been simultaneously age- and field- normalized?

RQ2: Developing a new normalization procedure. As the procedure of Radicchi et al. (2008) failed to correctly age- and field-normalize citation count and to the best of our knowledge there are no better ones, how can we develop a better one?


RQ3: Developing new knowledge order. How can we use time-correlations present in citation data to develop citation-based indicators?

The exchange of knowledge

RQ4: knowledge exchange among firms. Previous results from our chair indicate that knowledge is rather a determinant than a consequence of R&D collaborations. How does this result change when using different methods to quantify knowledge?

RQ5: knowledge exchange among scientists. How can we extend the model and the analysis of Tomasello et al., (2015) to the scientific domain?

The transfer of knowledge

RQ6: temporal correlations in the transfer of knowledge. How should we model scientists' academic mobility in order to retain temporal correlations and a network perspective?

RQ7: new agent-based model for knowledge transfer. Let us assume that temporal correlations in the migration trajectories of scientists break the transitivity assumption. Then, how can we use an agent-based model to reproduce these type of trajectories?