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Critical infrastructure failures from natural hazard events affect the economic and social well-being of communities. This is particularly true in lower income countries, where infrastructure may be less resis...
Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread the disease between population...
The quantitative study of traffic dynamics is crucial to ensure the efficiency of urban transportation networks. The current work investigates the spatial properties of congestion, that is, we aim to character...
The use of networks for modelling and analysing relations among data is currently growing. Recently, the use of a single networks for capturing all the aspects of some complex scenarios has shown some limitati...
This paper introduces the notions of chained and semi-chained graphs. The chain of a graph, when existent, refines the notion of bipartivity and conveys important structural information. Also the notion of a c...
It is well-established that the process of learning and capability building is core to economic development and structural transformation. Since knowledge is ‘sticky’, a key component of this process is learni...
We use the United Nations COMTRADE database for analysis of the multiproduct world trade network. With this data, considered for years 2012–2018, we determined the world trade impact of the Kernel of EU 9 coun...
The British party system is known for its discipline and cohesion, but it remains wedged on one issue: European integration. We offer a methodology using social network analysis that considers the individual i...
What do football passes and financial transactions have in common? Both are networked walk processes that we can observe, where records take the form of timestamped events that move something tangible from one...
Hospitals constitute highly interconnected systems that bring into contact an abundance of infectious pathogens and susceptible individuals, thus making infection outbreaks both common and challenging. In rece...
Urban street networks of unplanned or self-organized cities typically exhibit astonishing scale-free patterns. This scale-freeness can be shown, within the maximum entropy formalism (MaxEnt), as the manifestat...
Over the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under co...
Thanks to the widespread availability of large-scale datasets on scholarly outputs, science itself has come under the microscope with the aim of capturing a quantitative understanding of its workings. In this ...
We describe an approach to generate a heterosexual network with a prescribed joint-degree distribution embedded in a prescribed large-scale social contact network. The structure of a sexual network plays an im...
Markov clustering is an effective unsupervised pattern recognition algorithm for data clustering in high-dimensional feature space. However, its community detection performance in complex networks has been dem...
Collecting and receiving information about the state of a transportation system is essential to effective planning for intelligent transportation systems, whether it be on the part of individual users or manag...
National stay-at-home orders, or lockdowns, were imposed in several countries to drastically reduce the social interactions mainly responsible for the transmission of the SARS-CoV-2 virus. Despite being essent...
We study network centrality measures that take into account the specific structure of networks with time-stamped edges. In particular, we explore how such measures can be used to identify nodes most relevant f...
Understanding the role of individual nodes is a key challenge in the study of spreading processes on networks. In this work we propose a novel metric, the reachability-heterogeneity (RH), to quantify the contr...
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In additio...
We present a modelling framework for the spreading of epidemics on temporal networks from which both the individual-based and pair-based models can be recovered. The proposed temporal pair-based model that is ...
The DeGroot model for opinion diffusion over social networks dates back to the 1970s and models the mechanism by which information or disinformation spreads through a network, changing the opinions of the agen...
Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected ...
Hate speech has long posed a serious problem for the integrity of digital platforms. Although significant progress has been made in identifying hate speech in its various forms, prevailing computational approa...
The relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately...
Online political advertising is becoming increasingly popular as political campaigns recognize the utility of social network platforms, like Facebook, for reaching and engaging with voters. Yet, contrary to th...
Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans ...
Relationships between legal entities can be represented as a large weighted directed graph. In this work, we model the global capital ownership network across a hundred of millions of such entities with the go...
The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previo...
The objective of this study is to examine the transmission risk of COVID-19 based on cross-county population co-location data from Facebook. The rapid spread of COVID-19 in the United States has imposed a majo...
Infectious disease surveillance is often case-based, focused on people diagnosed and their contacts in a predefined time window, and treated as independent across infections. Network analysis of partners and c...
To what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning...
During the COVID-19 pandemic, political polarization has emerged as a significant threat that inhibits coordinated action of central and local institutions reducing the efficacy of non-pharmaceutical intervent...
The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlemen...
Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time. This temporal nature of a blockchain model enables us to understand the behavior (malicio...
Evidence from 184 countries over the span of 25 years is gathered and analyzed to understand North–North, South–South, and North–South international migration flows. Conceptually, the analysis borrows from net...
Characterizing the movement patterns of animals is crucial to improve our understanding of their behavior and thus develop adequate conservation strategies. Such investigations, which could not have been imple...
Vaccination has become one of the most prominent measures for preventing the spread of infectious diseases in modern times. However, mass vaccination of the population may not always be possible due to high co...
Contact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of informa...
The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on uniform mix...
Many real systems are extremely vulnerable against attacks, since they are scale-free networks as commonly existing topological structure in them. Thus, in order to improve the robustness of connectivity, seve...
COVID-19 is one of the deadliest pandemics in modern human history that has killed nearly a million people and rapidly inundated the healthcare resources around the world. Current lockdown measures to curb inf...
In this paper we propose a novel method to forecast the result of elections using only official results of previous ones. It is based on the voter model with stubborn nodes and uses theoretical results develop...
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 t...
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 pu...
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 subjecti...
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 technolo...
Epidemic spread on networks is one of the most studied dynamics in network science and has important implications in real epidemic scenarios. Nonetheless, the dynamics of real epidemics and how it is affected ...
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 ...
2022 Citation Impact
2.2 - 2-year Impact Factor
1.117 - SNIP (Source Normalized Impact per Paper)
0.603 - SJR (SCImago Journal Rank)
2023 Speed
12 days submission to first editorial decision for all manuscripts (Median)
119 days submission to accept (Median)
2023 Usage
581,134 downloads
776 Altmetric mentions
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