scholarly journals Models and Methods for Sparse (Hyper)Network Science in Business, Industry, and Government

2022 ◽  
Vol 69 (02) ◽  
pp. 1
Author(s):  
Sinan G Aksoy ◽  
Aric Hagberg ◽  
Cliff A Joslyn ◽  
Bill Kay ◽  
Emilie Purvine ◽  
...  
Keyword(s):  
Network Science

Network Science Center Research Team's Visit to Addis Ababa, Ethiopia

2012 ◽  
Author(s):  
Daniel Evans ◽  
Evan Szablowski ◽  
Zachary Langhans
Keyword(s):  
Network Science ◽  
Addis Ababa ◽  
Science Center ◽  
Center Research

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

The Spatial Dimensions of Social Networks

2020 ◽  
pp. 367-383
Author(s):  
Zachary P. Neal
Keyword(s):  
Social Networks ◽  
Topological Space ◽  
Network Science ◽  
Spatial Dimensions

The first law of geography holds that everything is related to everything else, but near things are more related than distant things, where distance refers to topographical space. If a first law of network science exists, it would similarly hold that everything is related to everything else, but near things are more related than distant things, but where distance refers to topological space. Frequently these two laws collide, together holding that everything is related to everything else, but topographically and topologically near things are more related than topographically and topologically distant things. The focus of the spatial study of social networks lies in exploring a series of questions embedded in this combined law of geography and networks. This chapter explores the questions that have been asked and the answers that have been offered at the intersection of geography and networks.

Pattern Recognition of Travel Mobility in a City Destination: Application of Network Motif Analytics

2021 ◽  
pp. 004728752110247
Author(s):  
Sangwon Park ◽  
Ren Ridge Zhong
Keyword(s):  
Network Science ◽  
Network Motif ◽  
Interdisciplinary Approach ◽  
Movement Patterns ◽  
Urban Tourism ◽  
Lengths Of Stay ◽  
Sustainable Planning ◽  
Total Movement ◽  
City Network ◽  
Travel Behaviors

Urban tourism is considered a complex system. Tourists who visit cities have diverse purposes, leading to multifaceted travel behaviors. Understanding travel movement patterns is crucial in developing sustainable planning for urban tourism. Built on network science, this article discusses 12 key topologies of travel patterns/flow occurring in a city network by applying network motif analytics. The 12 significant types of travel mobility can account for approximately 50% of the total movement patterns. In addition, this study presents variations in travel movement patterns depending on not only different lengths of stay in topological structures of travel mobility, but also relative proportions of each type. As a result, this article suggests an interdisciplinary approach that adopts the network science method to better understand city travel behaviors. Important methodological and practical implications that could be useful for city destination planners are suggested.

scholarly journals The Role of Network Science in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1045
Author(s):  
Marta B. Lopes ◽  
Eduarda P. Martins ◽  
Susana Vinga ◽  
Bruno M. Costa
Keyword(s):  
Personalized Medicine ◽  
Drug Development ◽  
Information Flow ◽  
Clinical Studies ◽  
Network Science ◽  
Cancer Genomics ◽  
High Dimensional ◽  
Network Discovery ◽  
Software Implementations

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

scholarly journals Drawing transmission graphs for COVID-19 in the perspective of network science

2020 ◽  
Vol 148 ◽  
Author(s):  
N. Gürsakal ◽  
B. Batmaz ◽  
G. Aktuna
Keyword(s):  
Probability Distribution ◽  
Network Science ◽  
Pareto Distribution ◽  
Transmission Probability ◽  
Transmission Network ◽  
Pareto Principle ◽  
Infected People ◽  
Discrete Pareto Distribution ◽  
Local Transmission

Abstract When we consider a probability distribution about how many COVID-19-infected people will transmit the disease, two points become important. First, there could be super-spreaders in these distributions/networks and second, the Pareto principle could be valid in these distributions/networks regarding estimation that 20% of cases were responsible for 80% of local transmission. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and second we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.

scholarly journals Likelihood-based approach to discriminate mixtures of network models that vary in time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naomi A. Arnold ◽  
Raul J. Mondragón ◽  
Richard G. Clegg
Keyword(s):  
Network Model ◽  
Change Point ◽  
Network Science ◽  
Preferential Attachment ◽  
Network Models ◽  
Explanatory Models ◽  
Growth Mechanisms ◽  
Artificial Data ◽  
Network Growth ◽  
Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

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