A Dynamic Embedding Model of the Media Landscape

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We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. Howeve ...

Understanding how high-quality newspapers present and discuss major news plays a role towards tackling disinformation, as it contributes to the characterization of the full ecosystem in which information circulates. In this paper, we present an analysis of ...

The drastic shift towards digital communication in our mediasphere has caused a profound change in the production and consumption of information, which in turn has substantial implications on the social and political landscape. Misinformation, as a side ef ...

Goods can exhibit positive externalities impacting decisions of customers in social networks. Suppliers can integrate these externalities in their pricing strategies to increase their revenue. Besides optimizing the prize, suppliers also have to consider t ...

Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body ...

The International Committee on Intellectual Cooperation (ICIC) is often framed as a step in the constitution of a “League of Minds” – a place where scientists and writers reign, and a necessary part of a successful and harmonious “League of Nations” – but ...

Despite numerous applications of physically-based models for incised landscapes, their applicability for overland flow on unchanneled surfaces is not known. This work challenges a widely used landscape evolution model for the case of non-uniform rainfall a ...

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...

Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogenei ...

In the era of misinformation and information inflation, the credibility assessment of the produced news is of the essence. However, fact-checking can be challenging considering the limited references presented in the news. This challenge can be transcended ...

We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statisti ...

Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to characterize these interactions. ...

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could sign ...

Motivation: Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in ...

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...

Protein-protein interaction (PPI) network alignment is a canonical operation to transfer biological knowledge among species. The alignment of PPI-networks has many applications, such as the prediction of protein function, detection of conserved network mot ...

We demonstrate the SciLens News Platform, a novel system for evaluating the quality of news articles. The SciLens News Platform automatically collects contextual information about news articles in real-time and provides quality indicators about their valid ...

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not ...

A Dynamic Embedding Model of the Media Landscape

Chattez avec Graph Search

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Network Alignment With Holistic Embeddings

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Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

WatchNet plus plus : efficient and accurate depth-based network for detecting people attacks and intrusion