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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...
We present a simple method to quantitatively capture the heterogeneity in the degree distribution of a network graph using a single parameter
Understanding a complex system of relationships between courses is of great importance for the university’s educational mission. This paper is dedicated to the study of course-prerequisite networks (CPNs), whe...
Stochastic dynamical systems in which local interactions give rise to complex emerging phenomena are ubiquitous in nature and society. This work explores the problem of inferring the unknown interaction struct...
The fractal nature of complex networks has received a great deal of research interest in the last two decades. Similarly to geometric fractals, the fractality of networks can also be defined with the so-called...
The Correction to this article has been published in Applied Network Science 2023 8:26
The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. However, few studies look at how both ...
Genealogical networks (i.e. family trees) are of growing interest, with the largest known data sets now including well over one billion individuals. Interest in family history also supports an 8.5 billion doll...
In this paper, we address the problem of earthquake declustering, and propose a k-nearest neighbors approach based on the selection of multiple-parent nodes with respect to each of the given earthquakes, which ca...
This paper is concerned with distributed detection of central nodes in complex networks using closeness centrality. Closeness centrality plays an essential role in network analysis. Distributed tasks such as l...
Systems Thinking (ST) has become essential for practitioners and experts when dealing with turbulent and complex environments. Twitter medium harbors social capital including systems thinkers, however there ar...
We investigate automatic methods to assess COVID vaccination views in Twitter content. Vaccine skepticism has been a controversial topic of long history that has become more important than ever with the COVID-...
Understanding the causes and consequences of, and devising countermeasures to, global warming is a profoundly complex problem. Network representations are sometimes the only way forward, and sometimes able to ...
While the ever-increasing emergence of online services has led to a growing interest in the development of recommender systems, the algorithms underpinning such systems have begun to be criticized for their ro...
The study investigates to what degree Indonesia’s large-scale decentralization and democratization changed corruption networks. A role structure approach is developed to move current analysis of dyad-level str...
This study outlines an efficient weighted network centrality measure approach and its application in network pharmacology for exploring mechanisms of action of the Ruellia prostrata (RP) and Ruellia bignoniiflora
The graph-theoretic based studies employing bipartite network approach mostly focus on surveying the statistical properties of the structure and behavior of the network systems under the domain of complex netw...
We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel in the case where it is prohibitively expensive to observe the entire graph. Theoretically, we provide justific...
This study aims to improve our understanding of criminal accomplice selection by studying the evolution of co-offending networks—i.e., networks that connect those who commit crimes together. To this end, we te...
We present a method to extract temporal hypergraphs from sequences of 2-dimensional functions obtained as solutions to Optimal Transport problems. We investigate optimality principles exhibited by these soluti...
Recent advancements in network science showed that the topological credentials of the elements (i.e., links) in a network carry important implications. Likewise, roadway segments (i.e., links) in a road networ...
Social media has provided a citizen voice, giving rise to grassroots collective action, where users deploy a concerted effort to disseminate online narratives and even carry out offline protests. Sometimes the...
By applying network analysis techniques to large input–output system, we identify key sectors in the local/regional economy. We overcome the limitations of traditional measures of centrality by using random-wa...
In this paper, we propose a general framework for the reconstruction of the underlying cross-regional transmission network contributing to the spread of an infectious disease. We employ an autoregressive model...
The deluge of network datasets demands a standard way to effectively and succinctly summarize network datasets. Building on similar efforts to standardize the documentation of models and datasets in machine le...
Fraud, corruption, and collusion are the most common types of crime in public procurement processes; they produce significant monetary losses, inefficiency, and misuse of the public treasury. However, empirica...
Social networks have been shown to enhance player experience in online games and to be important for the players, who often build complex communities. In online and mobile games, the behavior of players is bur...
The detection of communities of nodes is an important task in understanding the structure of networks. Multiple approaches have been developed to tackle this problem, many of which are in common usage in real-...
Many countries that had early access to COVID-19 vaccines implemented vaccination strategies that prioritized health care workers and the elderly. As barriers to access eased, vaccine prioritization strategies...
A road network can be represented as a weighted directed graph with the nodes being the traffic intersections, the edges being the road segments, and the weights being some attribute of a road segment. Such a ...
When studying large research corpora, “distant reading” methods are vital to understand the topics and trends in the corresponding research space. In particular, given the recognised benefits of multidisciplin...
The COVID-19 pandemic has once again brought the significance of biopharmaceutical and medical technology sectors to the spotlight. Seeing that some of the most critical medical breakthroughs such as the speed...
The instant messaging platform Telegram has become popular among the far-right movements in the US and UK in recent years. These groups use public Telegram channels and group chats to disseminate hate speech, ...
Correlation networks derived from multivariate data appear in many applications across the sciences. These networks are usually dense and require sparsification to detect meaningful structure. However, current...
The ERASMUS program is the most extensive cooperation network of European higher education institutions. The network involves 90% of European universities and hundreds of thousands of students. The allocated m...
Modelling complex biological systems is necessary for their study and understanding. Biomodels is a repository of peer-reviewed models represented in the Systems Biology Markup Language (SBML). Most of these m...
Centrality, in some sense, captures the extent to which a vertex controls the flow of information in a network. Here, we propose Local Detour Centrality as a novel centrality-based betweenness measure that cap...
High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain fals...
In this work, we introduce a new methodology for inferring the interaction structure of discrete valued time series which are Poisson distributed. While most related methods are premised on continuous state st...
Part of the reason women are disadvantaged in the labor market is because gender inequalities define social networks of the workplace. In the current project, I consider how gender shapes professional networks...
Recently proposed computational techniques allow the application of various maximum entropy network models at a larger scale. We focus on disinformation campaigns and apply different maximum entropy network mo...
In complex networks, the role of a node is based on the aggregation of structural features and functions. However, in real networks, it has been observed that a single node can have multiple roles. Here, the r...
The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from u...
Graph representation learning has become a topic of great interest and many works focus on the generation of high-level, task-independent node embeddings for complex networks. However, the existing methods con...
Network community detection often relies on optimizing partition quality functions, like modularity. This optimization appears to be a complex problem traditionally relying on discrete heuristics. And although...
Mountaineering is a sport of contrary forces: teamwork plays a large role in mental fortitude and skills, but the actual act of climbing, and indeed survival, is largely individualistic. This work studies the ...
Social network analysis is increasingly applied to modeling regional relationships. However, in this scenario, we cannot ignore the geographical economic and technological nature of the relationships. In this ...
Path problems are fundamental to the study of graphs. Temporal graphs are graphs in which the edges connecting the vertices change with time. Min hop paths problem in a temporal graph is the problem of finding...
Citation Impact 2023
Journal Impact Factor: 1.3
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Source Normalized Impact per Paper (SNIP): 0.955
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