LDA2Net Digging under the surface of COVID-19 scientific literature topics via a network-based approach.

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically. These literary items encompass a varied set of topics, ranging from vaccination to protective equipment efficacy as well as lockdown policy evaluations. As a result, the development of automatic methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in on its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis, to investigate topics under their surface. More specifically, LDA2Net exploits the frequencies of consecutive words pairs (i.e. bigram) to build those network structures underlying the hidden topics extracted from large volumes of text by Latent Dirichlet Allocation (LDA). Results are promising and suggest that the topic model efficacy is magnified by the network-based representation. In particular, such enrichment is noticeable when it comes to displaying and exploring the topics at different levels of granularity.

Representation of Jews and Anti-Jewish Bias in 19th Century French Public Discourse: Distant and Close Reading.

We explore through the lens of distant reading the evolution of discourse on Jews in France during the XIX century. We analyze a large textual corpus including heterogeneous sources-literary works, periodicals, songs, essays, historical narratives-to trace how Jews are associated to different semantic domains, and how such associations shift over time. Our analysis deals with three key aspects of such changes: the overall transformation of embedding spaces, the trajectories of word associations, and the comparative projection of different religious groups over different, historically relevant semantic dimensions or streams of discourse. This allows to show changes in the association between words and semantic domains (referring e.g. to economic and moral behaviors), the evolution of stereotypes, and the dynamics of bias over a long time span characterized by major historical transformations. We suggest that the analysis of large textual corpora can be fruitfully used in a dialogue with more traditional close reading approaches-by pointing to opportunities of in-depth analyses that mobilize more qualitative approaches and a detailed inspection of the sources that distant reading inevitably tends to aggregate. We offer a short example of such a dialogue between different approaches in our discussion of the Second Empire transformations, where we mobilize the historian's tools to start disentangling the complex interactions between changes in French society, the nature of sources, and representations of Jews. While our example is limited in scope, we foresee large potential payoffs in the cooperative interaction between distant and close reading.

Can a Quantum Walk Tell Which Is Which?A Study of Quantum Walk-Based Graph Similarity.

We consider the problem of measuring the similarity between two graphs using continuous-time quantum walks and comparing their time-evolution by means of the quantum Jensen-Shannon divergence. Contrary to previous works that focused solely on undirected graphs, here we consider the case of both directed and undirected graphs. We also consider the use of alternative Hamiltonians as well as the possibility of integrating additional node-level topological information into the proposed framework. We set up a graph classification task and we provide empirical evidence that: (1) our similarity measure can effectively incorporate the edge directionality information, leading to a significant improvement in classification accuracy; (2) the choice of the quantum walk Hamiltonian does not have a significant effect on the classification accuracy; (3) the addition of node-level topological information improves the classification accuracy in some but not all cases. We also theoretically prove that under certain constraints, the proposed similarity measure is positive definite and thus a valid kernel measure. Finally, we describe a fully quantum procedure to compute the kernel.