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As networks grow in size and complexity, backbones become an essential network representation. Indeed, they provide a simplified yet informative overview of the underlying organization by retaining the most si...
The digital age provides new challenges as information travels more quickly in a system of increasing complexity. But it also offers new opportunities, as we can track and study the system more efficiently. Se...
Financial networks can be constructed to model the intertemporal price dependencies within an asset market, giving rise to a causal network. These networks are traditionally inferred through multivariate predi...
Understanding how influence is seeded and spreads through social networks is an increasingly important study area. While there are many methods to identify seed nodes that are used to initialize a spread of in...
Network visualization is an important tool for extracting information from the structure and configuration of a network, especially when the network includes weighted edges and nodes with attribute information...
In this work, we explore the extent to which the spectrum of the graph Laplacian can characterize the probability distribution of random graphs for the purpose of model evaluation and model selection for netwo...
Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of l...
The explosive growth of the Web and of social networks motivates the need for analyzing the macroscopic structure of their underlying graphs. Although the characterization of the structure of a graph with resp...
The aim of our work is to develop a generic conceptual agent-based model to formalize the interaction of vector and host given climate change. The model consists in creating a hypothetical example of a vector-...
Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may repre...
Breast cancer is a prominent global health concern, as the data from the International Agency for Research on Cancer (IARC) shows that breast cancer is the leading cancer type with new cases in 2020 and among ...
With real-world network systems typically comprising a large number of interactive components and inherently dynamic, Graph Continual Learning (GCL) has gained increasing popularity in recent years. Furthermor...
Online social networks have become indispensable in modern life, facilitating knowledge sharing, social communication, and business marketing. To gain a deeper understanding of individuals' behavior within soc...
Networks provide an understandable and, in the case of small size, visualizable representation of data, which allows us to obtain essential information about the relationships between pairs of nodes, e.g., the...
Understanding the spread of SARS-CoV-2 has been one of the most pressing problems of the recent past. Network models present a potent approach to studying such spreading phenomena because of their ability to r...
We study the dynamical assembly of weighted bipartite networks to understand the hidden mechanisms of pollination, expanding the Bianconi–Barabási model where nodes have intrinsic properties. Allowing for a no...
We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primari...
When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrela...
Network-based time series models have experienced a surge in popularity over the past years due to their ability to model temporal and spatial dependencies, arising from the spread of infectious disease. The g...
This study examines sports and physical activities among Chinese aged 18–65, using network analysis on a significant random sample. It categorizes sports into 11 groups based on public selection, with a commun...
In this work, we investigate the analysis of generators for dynamic graphs, which are defined as graphs whose topology changes over time. We focus on generated graphs whose order (number of nodes) varies over ...
Importance measures (IMs) in networks are indices that allow the analysis and evaluation of the network components that are most critical to the performance of the network. Such information is useful for a dec...
In recommender systems, users rate items, and are subsequently served other product recommendations based on these ratings. Even though users usually rate a tiny percentage of the available items, the system t...
The social networks that interconnect groups of people are often “multi-layered”—comprised of a variety of relationships and interaction types. Although researchers increasingly acknowledge the presence of mul...
Real-world networks often encounter disruptions that can have profound societal impacts. Therefore, enhancing network robustness against random failures and targeted attacks is crucial. Bypass rewiring, in whi...
Influence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has attracted attention on tempora...
The main objective of this research is to analyse the connectivity of cities in a coupled network composed of planar (railways) and non-planar (maritime) topologies. It examines the state of the network during...
We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by ...
Reconstructing dynamics of complex systems from sparse, incomplete time series data is a challenging problem with applications in various domains. Here, we develop an iterative heuristic method to infer the un...
Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses...
Digital transformation refers to an organization’s use of digital technology to improve its products, services, and operations, aligning them with evolving business requirements. To demonstrate this transforma...
In this paper, we extended the technique of measuring similarity between topological spaces using bottle neck distance between persistence diagrams to hypergraph networks. Finding a relationship between the bo...
The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust mo...
In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptu...
Trust seems to become established even in scenarios where the prerequisites for trust are complicated by conditions that evoke scepticism. Nonetheless, trust emerges, a phenomenon that is to be comprehended an...
Inferring relations from correlational data allows researchers across the sciences to uncover complex connections between variables for insights into the underlying mechanisms. The researchers often represent ...
Vertex classification using graph convolutional networks is susceptible to targeted poisoning attacks, in which both graph structure and node attributes can be changed in an attempt to misclassify a target nod...
The influence and pervasiveness of misinformation on social media platforms such as Twitter have been well-documented in recent years. These platforms’ real-time, rapid-fire nature and the personalized, echo-c...
Pairwise temporal interactions between entities can be represented as temporal networks, which code the propagation of processes such as epidemic spreading or information cascades, evolving on top of them. The...
A growing number of social media studies in the U.S. rely on the characterization of the opinion of individual users, for example, as Democrat- or Republican-leaning, or in continuous scales ranging from most ...
Random graphs are statistical models that have many applications, ranging from neuroscience to social network analysis. Of particular interest in some applications is the problem of testing two random graphs f...
The growing data size poses challenges for storage and computational processing time in semi-supervised models, making their practical application difficult; researchers have explored the use of reduced networ...
In recent years, social networks have become popular among Internet users, and various studies have been performed on the analysis of users’ behavior in social networks. Information diffusion analysis is one o...
Often, due to prohibitively large size or to limits to data collecting APIs, it is not possible to work with a complete network dataset and sampling is required. A type of sampling which is consistent with Twi...
Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data ...
The degree of polarization in many societies has become a pressing concern in media studies. Typically, it is argued that the internet and social media have created more media producers than ever before, allow...
The labor market has transformed with the advent of the gig economy, characterized by short-term and flexible work arrangements facilitated by online platforms. As this trend becomes increasingly prevalent, it...
Temporal networks are networks whose topology changes over time. Two nodes in a temporal network are connected at a discrete time step only if they have a contact/interaction at that time. The classic temporal...
Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position ...
Ten years after the collapse of the Rana Plaza textile factory in Dhaka, Bangladesh that killed over 1000 factory workers, the event has become a symbol for the desolate working conditions in fast fashion prod...
Citation Impact 2023
Journal Impact Factor: 1.3
5-year Journal Impact Factor: N/A
Source Normalized Impact per Paper (SNIP): 0.955
SCImago Journal Rank (SJR): 0.526
Speed 2023
Submission to first editorial decision (median days): 11
Submission to acceptance (median days): 119
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Downloads: 581,134
Altmetric mentions: 776
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