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In this work we give a community detection algorithm in which the communities both respects the intrinsic order of a directed acyclic graph and also finds similar nodes. We take inspiration from classic simila...
According to United Nations, corruption is a systemic and adaptive phenomenon that requires comprehensive and multidisciplinary approaches for its effective prevention and combat. However, traditional approach...
Correlation networks have been a popular way of inferring a financial network due to the simplicity of construction and the ease of interpretability. However two variables which share a common cause can be cor...
Network analysis of social media provides an important new lens on politics, communication, and their interactions. This lens is particularly prominent in fast-moving events, such as conversations and action i...
Hypergraphs offer a natural modeling language for studying polyadic interactions between sets of entities. Many polyadic interactions are asymmetric, with nodes playing distinctive roles. In an academic collab...
Following publication of the original article (Larson Applied Network Science 2017), the author reported to publish a brief corrigendum in the original article;
The original article was published in Applied Network Science 2017 2:14
We model the flow of human capital and resources across multimodal transportation networks throughout the Greater Tokyo Area. Our transportation networks include trains, buses, and roads integrated with a walk...
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification dev...
The creation of social ties is largely determined by the entangled effects of people’s similarities in terms of individual characters and friends. However, feature and structural characters of people usually a...
Following publication of the original article [1], the author reported to remove the name from the acknowledgements in the original article.
The original article was published in Applied Network Science 2019 4:98
We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale ca...
Although the cluster theory literature is bountiful in economics and regional science, there is still a lack of understanding of how the geographical scales of analysis (neighbourhood, city, region) relate to ...
The objective of this research is to evaluate whether complex dynamics of urban drainage networks (UDNs) can be expressed in terms of their structure, i.e. topological characteristics. The present study focuse...
Cryptocurrencies as a new way of transferring assets and securing financial transactions have gained popularity in recent years. Transactions in cryptocurrencies are publicly available, hence, statistical stud...
Smart cities and traffic applications can be modelled by dynamic graphs for which vertices or edges can be added, removed or change their properties. In the smart city or traffic monitoring problem, we wish to de...
Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine lear...
In a metropolis, people movements design intricate patterns that change on very short temporal scales. Population mobility obviously is not random, but driven by the land uses of the city. Such an urban ecosys...
Understanding criminal activities, their structure and dynamics are fundamental for designing tools for crime prediction that can also guide crime prevention. Here, we study crimes committed in city community ...
In this work we introduce and analyze a new and comprehensive multilayer dataset covering a wide spectrum of international relationships between coutries. We select two cross sections of the dataset correspond...
Learning low-dimensional representations of graphs has facilitated the use of traditional machine learning techniques to solving classic network analysis tasks such as link prediction, node classification, com...
In this paper we present a generalization of the classical configuration model. Like the classical configuration model, the generalized configuration model allows users to specify an arbitrary degree distribut...
We provide a novel family of generative block-models for random graphs that naturally incorporates degree distributions: the block-constrained configuration model. Block-constrained configuration models build ...
There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspect...
Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience w...
Modern software development is often a collaborative effort involving many authors through the re-use and sharing of code through software libraries. Modern software “ecosystems” are complex socio-technical sy...
The voter model has been widely studied due to its simple formulation and attainable theoretical treatment. The study of the “active links”, edges that connect nodes in different states, has been a key element...
Labour mobility within a large city or metropolitan area is a necessary condition for the optimal exploitation of agglomeration economies. We propose a method to establish which municipalities should be consid...
Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists...
We propose a framework to study the spreading of urban legends, i.e., false stories that become persistent in a local popular culture, where social groups are naturally segregated by virtue of many (both mutable ...
During their lifespan, stem- or progenitor cells have the ability to differentiate into more committed cell lineages. Understanding this process can be key in treating certain diseases. However, up until now o...
A long-standing question for urban and regional planners pertains to the ability to describe urban patterns quantitatively. Cities’ transport infrastructure, particularly street networks, provides an invaluabl...
Following publication of the original article [1], the author reported that figure 5 numbered correction was not implemented during the production process. The corrected figure can be found below.
The original article was published in Applied Network Science 2019 4:96
One can point to a variety of historical milestones for gender equality in STEM (science, technology, engineering, and mathematics), however, practical effects are incremental and ongoing. It is important to q...
Social networks have become widely used platforms for their users to share information. Learning the information diffusion process is essential for successful applications of viral marketing and cyber security...
Many complex processes can be viewed as dynamical systems on networks. However, in real cases, only the performances of the system are known, the network structure and the dynamical rules are not observed. The...
Urbanization and the continued growth of cities, both demographically and spatially, are topics of research studied across a range of disciplines in the urban millennium — a time in history when the majority o...
We propose a unified framework to represent a wide range of continuous-time discrete-state Markov processes on networks, and show how many network dynamics models in the literature can be represented in this u...
Important data mining problems such as nearest-neighbor search and clustering admit theoretical guarantees when restricted to objects embedded in a metric space. Graphs are ubiquitous, and clustering and class...
Directed contact networks (DCNs) are temporal networks that are useful for analyzing and modeling phenomena in transportation, communications, epidemiology and social networking. Specific sequences of contacts ca...
Epidemiological contact network models have emerged as an important tool in understanding and predicting spread of infectious disease, due to their capacity to engage individual heterogeneity that may underlie...
The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Sp...
Complex social-ecological systems—such as cities and regions—change in time whether or not we intervene through plans and policies. This is due in part to the numerous individual and organizational actors who ...
Through direct communication, device-to-device (D2D) technology can increase the overall throughput, enhance the coverage, and reduce the power consumption of cellular communications. Security will be of param...
Humans predominantly form their beliefs based on communication with other humans rather than direct observations, even on matters of facts, such as the shape of the globe or the effects of child vaccinations. ...
Distributed algorithms for network science applications are of great importance due to today’s large real-world networks. In such algorithms, a node is allowed only to have local interactions with its immediat...
We consider optimal attacks or immunization schemes on different models of random graphs. We derive bounds for the minimum number of nodes needed to be removed from a network such that all remaining components...
Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the rec...
The Correction to this article has been published in Applied Network Science 2020 5:4
This paper proposes a methodological approach to explore the ability to detect social media users based on pedestrian networks and neighborhood attributes. We propose the use of a detection function belonging ...
The Correction to this article has been published in Applied Network Science 2019 4:113
The objective of a community detection algorithm is to group similar nodes that are more connected to each other than with the rest of the network. Several methods have been proposed but many are of high compl...
Complex networks are used as means for representing multimodal, real-life systems. With increasing amounts of data that lead to large multilayer networks consisting of different node and edge types, that can a...
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