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Influence maximization (IM) is the task of finding the most important nodes in order to maximize the spread of influence or information on a network. This task is typically studied on static or temporal networ...
Network science offers valuable tools for planning and managing public transportation systems, with measures such as network centralities proposed as complementary predictors of ridership. This paper explores ...
Apart from nodes and links, for many networked systems, we have access to data on paths, i.e., collections of temporally ordered variable-length node sequences that are constrained by the system’s topology. Un...
Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as v...
Social media platforms offer cost-effective digital marketing opportunities to monitor the market, create user communities, and spread positive opinions. They allow companies with fewer budgets, like startups,...
During the April 2023 Brazil–China summit, the creation of a trade currency supported by the BRICS countries was proposed. Using the United Nations Comtrade database, providing the frame of the world trade net...
Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an e...
Deanonymization is one of the major research challenges in the Bitcoin blockchain, as entities are pseudonymous and cannot be identified from the on-chain data. Various approaches exist to identify multiple ad...
We investigate the development of cooperative behavior in networks over time. In our controlled laboratory experiment, subjects can cooperate by sending costly messages that contain valuable information for th...
Crypto assets have lately become the chief interest of investors around the world. The excitement around, along with the promise of the nascent technology led to enormous speculation by impulsive investors. De...
The decision of where to locate the catchment area of an urban network exerts significant influence on the indicator values and in this research this influence is referred to as the placement effect. Placement...
This paper empirically investigates the role played by cross-country spillovers in shaping spatiotemporal differences in country income. While existing literature focused on effects captured by direct spillove...
This research aimed to gain a deeper understanding of how and for what reasons the world trade networks of medical products were reorganized during the novel coronavirus (COVID-19) pandemic. To do this, first,...
Hospital databases provide complex data on individual patients, which can be analysed to discover patterns and relationships. This can provide insight into medicine that cannot be gained through focused studie...
It is often of interest to sample vertices from a graph with a bias towards higher-degree vertices. One well-known method, which we call random neighbor or RN, involves taking a vertex at random and exchanging...
Rapid urbanization has posed challenges to accessibility to critical services that require in-depth analysis. Complex networks theory has been used to evaluate the evolution of network topologies or the overal...
Tobacco use is the leading cause of preventable deaths in developed countries. Many interventions and policies have been implemented to reduce the levels of smoking but these policies rarely rely on models tha...
Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limi...
Self-propagating malware (SPM) is responsible for large financial losses and major data breaches with devastating social impacts that cannot be understated. Well-known campaigns such as WannaCry and Colonial P...
Nestedness has been observed in a variety of networks but has been primarily viewed in the context of bipartite networks. Numerous metrics quantify nestedness and some clustering methods identify fully nested ...
The present study aims to infer individuals’ social networks from their spatio-temporal behavior acquired via wearable sensors. Previously proposed static network metrics (e.g., centrality measures) cannot cap...
While there have been numerous sequential algorithms developed to estimate community structure in networks, there is little available guidance and study of what significance level or stopping parameter to use ...
Aiming at knowledge discovery for temporal sequences of cooking recipes published in social media platforms from the viewpoint of network science, we consider an analysis of temporal higher-order networks of i...
The original article was published in Applied Network Science 2023 8:40
Social media platforms centered around content creators (CCs) faced rapid growth in the past decade. Currently, millions of CCs make livable incomes through platforms such as YouTube, TikTok, and Instagram. As...
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm...
Characterizing a public transportation network, such as an urban network with multiple lines, requires the origin–destination trip counts during a given period. Yet, if automatic counting makes the embarkment ...
Designing network systems able to sustain functionality after random failures or targeted attacks is a crucial aspect of networks. This paper investigates several strategies of link selection aiming at enhanci...
New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the ne...
Hypergraph is a graph structure that can efficiently express the relationship of multiple nodes and has attracted attention in recent years. As with normal graphs, the structure changes every moment, and it is...
We discuss the added value of various approaches for identifying similarities in social network communities based on the content they produce. We show the limitations of observing communities using topology-on...
The Correction to this article has been published in Applied Network Science 2023 8:47
We investigate the statistical learning of nodal attribute functionals in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models,...
Non-fungible tokens (NFTs) are a type of digital asset that can prove ownership of both virtual and physical assets using blockchains. Even though creating and trading NFTs have experienced a significant incre...
In times of a global pandemic, public transit can be crucial to spreading viruses, especially in big cities. Many works have shown that the human infection risk could be extremely high due to the length of exp...
Some phenotypical changes may be related to changes in the associations among genes. The set of such associations is referred to as gene interaction (or association) networks. An association network represents...
In recent years, the global trade landscape has undergone significant changes, particularly in the aftermath of the 2008 financial crisis and more recently as a consequence of Covid-19 pandemic. To understand ...
In the Bitcoin protocol, dust refers to small amounts of currency that are lower than the fee required to spend them in a transaction. Although “economically irrational”, dust is commonly used for achieving un...
Interactive programming environments are powerful tools for promoting innovative network thinking, teaching science of complexity, and exploring emergent phenomena. This paper reports on our recent development...
The widely used characterization of scale-free networks as “robust-yet-fragile” originates primarily from experiments on instances generated by preferential attachment. According to this characterization, scal...
Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, ai...
Bipolarization is a phenomenon in which either a large or very small information cascade appears randomly when the retweet rate is high. This phenomenon, which has been observed only in simulations, has the po...
The share and variants of coupling points (CPs) between different energy carrier networks (such as the gas or power grids) are increasing, which results in the necessity of the analysis of so-called multi-ener...
Role discovery is the task of dividing the set of nodes on a graph into classes of structurally similar roles. Modern strategies for role discovery typically rely on graph embedding techniques, which are capab...
Link prediction (LP) has many applications in various fields. Much research has been carried out on the LP field, and one of the most critical problems in LP models is handling one-to-many and many-to-many rel...
The original article was published in Applied Network Science 2023 8:17
The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with simi...
Recommendation letters were an important instrument for orchestrating Jewish emigration from Germany in the late nineteenth and early twentieth century. Here, we present network-based analyses of manually coll...
Protecting medical privacy can create obstacles in the analysis and distribution of healthcare graphs and statistical inferences accompanying them. We pose a graph simulation model which generates networks usi...
The use of data and algorithms in the social sciences allows for exciting progress, but also poses epistemological challenges. Operations that appear innocent and purely technical may profoundly influence fina...
Empirical studies of the spread of something through social networks, a process often called diffusion, tend to rely on network data assembled from the measurement of multiple kinds of social ties. These can b...
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