Maheshwari, Parul Albert, Réka
Published in
Applied Network Science
The first mitigation response to the Covid-19 pandemic was to limit person-to-person interaction as much as possible. This was implemented by the temporary closing of many workplaces and people were required to follow social distancing. Networks are a great way to represent interactions among people and the temporary severing of these interactions....
Kodate, Shun Chiba, Ryusuke Kimura, Shunya Masuda, Naoki
Published in
Applied Network Science
Providers of online marketplaces are constantly combatting against problematic transactions, such as selling illegal items and posting fictive items, exercised by some of their users. A typical approach to detect fraud activity has been to analyze registered user profiles, user’s behavior, and texts attached to individual transactions and the user....
Dyer, Joel Kolic, Blas
Published in
Applied Network Science
Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring...
Alghamdi, Elham Rushe, Ellen Mac Namee, Brian Greene, Derek
Published in
Applied Network Science
In many real applications of semi-supervised learning, the guidance provided by a human oracle might be “noisy” or inaccurate. Human annotators will often be imperfect, in the sense that they can make subjective decisions, they might only have partial knowledge of the task at hand, or they may simply complete a labeling task incorrectly due to the ...
Meghanathan, Natarajan
Published in
Applied Network Science
We first propose a binary search algorithm to determine the minimum fraction of nodes in a network to be used as initial adopters (fIAmin\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \...
Ashihara, Kazuki El Vaigh, Cheikh Brahim Chu, Chenhui Renoust, Benjamin Okubo, Noriko Takemura, Noriko Nakashima, Yuta Nagahara, Hajime
Published in
Applied Network Science
Topic modeling that can automatically assign topics to legal documents is very important in the domain of computational law. The relevance of the modeled topics strongly depends on the legal context they are used in. On the other hand, references to laws and prior cases are key elements for judges to rule on a case. Taken together, these references...
Melnyk, Kateryna Klus, Stefan Montavon, Grégoire Conrad, Tim O. F.
Published in
Applied Network Science
More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored ov...
Gensollen, Nicolas Latapy, Matthieu
Published in
Applied Network Science
We study the interplay between social ties and financial transactions made through a recent cryptocurrency called Ğ1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\brev...
Canillas, Rémi Hasan, Omar Sarrat, Laurent Brunie, Lionel
Published in
Applied Network Science
Supplier Impersonation Fraud (SIF) is a rising issue for Business-to-Business companies. The use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose a data-driven fraud detection system whose goal is to provide an accurate estimation of financial transaction legitimacy by us...
Chen, Lu Murata, Masayuki
Published in
Applied Network Science
Catastrophic forgetting occurs when learning algorithms change connections used to encode previously acquired skills to learn a new skill. Recently, a modular approach for neural networks was deemed necessary as learning problems grow in scale and complexity since it intuitively should reduce learning interference by separating functionality into p...