1,720,952 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
asl-epfl/sml_icassp2021: Network Classifiers Based on Social Learning
No full text<p>This code can be used to generate simulations similar to Fig. 2 in the following paper:</p>
<p>Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, and Ali H. Sayed, "Network Classifiers Based on Social Learning,'' in Proc. IEEE ICASSP, Toronto, Canada, May 2021. DOI : <a href="https://doi.org/10.1109/ICASSP39728.2021.9414126">10.1109/ICASSP39728.2021.9414126</a></p>
<p>The three panels in Fig. 2 are generated executing file 'main.py'.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>March 2022 (Author: Virginia Bordignon)</p>
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
asl-epfl/partial_it_2023: Partial Information Sharing over Social Learning Networks (IT 2023)
No full textThis code can be used to generate simulations similar to Figs. 4, 6, 7, 8, 9, 10 in the following paper:
Virginia Bordignon, Vincenzo Matta, and Ali H. Sayed, "Partial Information Sharing over Social Learning Networks,'' in IEEE Transactions on Information Theory, 2023. [DOI: 10.1109/TIT.2022.3227587]
Fig. 4 is generated executing file 'fig_4.py'.
Figs. 6 and 7 are generated executing file 'figs_6_7.py'.
Figs. 8 and 9 are generated executing file 'figs_8_9.py'.
Fig. 10 is generated executing file 'fig_10.py'.
Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.
Jan 2023 (Author: Virginia Bordignon
asl-epfl/hmm_over_graphs_dslw2022: Hidden Markov Modeling Over Graphs
No full text<p>This code can be used to generate simulations similar to Figs. 1, 2 and 3 in the following paper:</p>
<p>M. Kayaalp, V. Bordignon, S. Vlaski and A. H. Sayed, "Hidden Markov Modeling Over Graphs," 2022 IEEE Data Science and Learning Workshop (DSLW), 2022, pp. 1-6. DOI: 10.1109/DSLW53931.2022.9820077</p>
<p>Figs. 1, 2 and 3 are generated executing file 'main.py'.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>November 2022 (Author: Virginia Bordignon)</p>
asl-epfl/asl-it-2021: Adaptive Social Learning (IT 2021)
No full textThis code can be used to generate simulations similar to Figs. 1, 2, 3, 4, 6, 7, 8, 9, 10, 12 and 13 in the following paper:
Virginia Bordignon, Vincenzo Matta, and Ali H. Sayed, "Adaptive social learning,'' in IEEE Transactions on Information Theory, 2021. DOI: 10.1109/TIT.2021.3094633
Figs. 1 and 2 are generated executing file 'figs1_2.py'.
Fig. 3 is generated executing file 'fig3.py'.
Fig. 4 is generated executing file 'fig4.py'.
Figs. 6-10 are generated executing file 'figs6_7_8_9_10.py'.
Figs. 12 and 13 are generated executing file 'figs12_13.py'.
Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.
July 2021 (Author: Virginia Bordignon
asl-epfl/nbnb_eusipco2023: Social Learning with Non-Bayesian Local Updates
No full text<p>This code can be used to generate simulations similar to Fig. 1 in the following paper:</p>
<p>V. Bordignon, M. Kayaalp, V. Matta, and A. H. Sayed, "Social learning with non-Bayesian local updates,'' Proc. EUSIPCO, pp. 1-5, Helsinki, Finland, Sep. 2023.</p>
<p>The images used in Fig. 1 are generated executing file 'main.py'.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>Jul 2023 (Author: Virginia Bordignon)</p>
asl-epfl/sml_it_2023: Learning from Heterogeneous Data Based on Social Interactions over Graphs (IT 2023)
No full text<p>This code can be used to generate simulations similar to Figs. 3-11 in the following paper:</p>
<p>Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, and Ali H. Sayed, "Learning from Heterogeneous Data Based on Social Interactions over Graphs,'' in IEEE Transactions on Information Theory, 2023. (DOI:<a href="https://doi.org/10.1109/TIT.2022.3232368">10.1109/TIT.2022.3232368</a>)</p>
<p>Figs. 3 to 7 are generated executing file 'figures_3_7.py'.</p>
<p>Fig. 8 is generated executing file 'figure_8.py'.</p>
<p>Fig. 9 is generated executing file 'figure_9.py'.</p>
<p>Fig. 10 is generated executing file 'figure_10.py'.</p>
<p>Fig. 11 is generated executing file 'figure_11.py'.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>April 2023 (Author: Virginia Bordignon)</p>
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