Networks and AI seminar series 2025/2026 edition: 11th September
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Liubov
Sep 9, 2025, 1:54:04 AMSep 9
to network-se...@googlegroups.com, Valerio Di Pasquale
Dear all,
After a long break we are restarting the seminar series.
This year we will change the format, making it more lively: 20 min main idea + 10 mins hands-on session of the seminar + active discussions.
Hope to see many of you there!
When?
11th of September 4pm CET
Where?
Online/register to get Teams meeting link We will make sure the seminar stays hybrid with onsite discussions.
What?
Presentation title: Hypergraph Motif Representation Learning
Presentation title: Hypergraph Motif Representation Learning
Abstract:
Hypergraphs provide a powerful abstraction for modeling high-order
interactions in complex systems, extending beyond simple pairwise
relations captured by traditional graphs. Within these structures,
hypergraph motifs (h-motifs) represent recurrent local connectivity
patterns that are critical for understanding the dynamics of domains
ranging from biology to social networks. While motif analysis has been
extensively explored in graphs, the prediction and representation of
h-motifs in hypergraphs remain largely uncharted. In this seminar, we
present recent advances on hypergraph motif representation learning,
focusing on the novel task of h-motif prediction. We formalize this
problem and introduce an end-to-end architecture that combines
hypergraph neural networks (HGCNs) with graph convolutions to
effectively capture correlations among hyperedges. To address the
challenge of robust training, we design an innovative negative sampling
strategy that generates non-trivial, close-to-positive motif samples.
Through extensive experiments on real-world datasets, our approach
demonstrates superior predictive performance over a wide range of
baselines, highlighting its effectiveness and robustness.
Biography: Valerio Di Pasquale is a computer science graduate with several years of professional experience. After completing his Bachelor's degree, he collaborated on research activities at ISISLab, Università degli Studi di Salerno, where he also earned his Master's degree. His research interests include Artificial Intelligence and Generative AI, with a focus on complex networks, Graph Neural Networks, and hypergraphs for the modeling and analysis of high-dimensional and relational data.
Biography: Valerio Di Pasquale is a computer science graduate with several years of professional experience. After completing his Bachelor's degree, he collaborated on research activities at ISISLab, Università degli Studi di Salerno, where he also earned his Master's degree. His research interests include Artificial Intelligence and Generative AI, with a focus on complex networks, Graph Neural Networks, and hypergraphs for the modeling and analysis of high-dimensional and relational data.
Link to the paper/github from ACM Digital Library: https://dl.acm.org/doi/10.1145/3690624.3709274
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Thank you,
All the best,
Network seminar organisers
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Liubov
Oct 6, 2025, 1:01:28 PMOct 6
to network-se...@googlegroups.com
Dear all,
We are continuing our seminar series. This week we will have an exciting talk from Mike Tamm (TLU, Estonia). Hope to see many of you there!
When?
9th of October 4pm CET
Where?
Online/register to get Teams meeting link We will make sure the seminar stays hybrid with onsite discussions.
What?
City representation in the Soviet propaganda: quantifying biases of the Soviet worldview
Representation of geographical locations in cultural data is almost always biased, creating a distorted representation of the world. Identifying and measuring such biases is crucial to understand both the data and the socio-cultural processes that have produced them. We suggest measuring geographical biases in a historical news media corpora by studying the representation of cities. Leveraging ideas of quantitative urban science, we develop a mixed quantitative-qualitative procedure, which allows us to get robust quantitative estimates of the biases. The procedure includes (i)formulation of a hypothesis based on qualitative analysis of the data, (ii) parameter extraction by optimization of an explicit loss function and (iii) model selection and pruning to avoid overfitting. These three steps form a feedback loop: the output of the last step (and, in particular, the outliers, which the model is unable to explain) can be used as an input for refining the hypothesis. We apply this procedure to a corpus of Soviet newsreel series 'Novosti Dnya' (News of the Day) and show that city representation grows super-linearly with city size and is further biased by city specialization and geographical location. This allows systematical identification of the geographical regions which are explicitly or covertly emphasized by Soviet propaganda and quantify their importance.
In the talk I will pay attention both to the fine methodological aspects, such as choice of loss function to allow for scarceness of the dataset and information-theoretical criteria of model selection, and to the discussion of the actual result and what they tell us about the underlying Soviet world view.
City representation in the Soviet propaganda: quantifying biases of the Soviet worldview
Representation of geographical locations in cultural data is almost always biased, creating a distorted representation of the world. Identifying and measuring such biases is crucial to understand both the data and the socio-cultural processes that have produced them. We suggest measuring geographical biases in a historical news media corpora by studying the representation of cities. Leveraging ideas of quantitative urban science, we develop a mixed quantitative-qualitative procedure, which allows us to get robust quantitative estimates of the biases. The procedure includes (i)formulation of a hypothesis based on qualitative analysis of the data, (ii) parameter extraction by optimization of an explicit loss function and (iii) model selection and pruning to avoid overfitting. These three steps form a feedback loop: the output of the last step (and, in particular, the outliers, which the model is unable to explain) can be used as an input for refining the hypothesis. We apply this procedure to a corpus of Soviet newsreel series 'Novosti Dnya' (News of the Day) and show that city representation grows super-linearly with city size and is further biased by city specialization and geographical location. This allows systematical identification of the geographical regions which are explicitly or covertly emphasized by Soviet propaganda and quantify their importance.
In the talk I will pay attention both to the fine methodological aspects, such as choice of loss function to allow for scarceness of the dataset and information-theoretical criteria of model selection, and to the discussion of the actual result and what they tell us about the underlying Soviet world view.
All the best,
Liubov
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