Still a Small World? University Course Enrollment Networks before and during the COVID-19 Pandemic

In normal times, the network ties that connect students on a college campus are an asset; during a pandemic, they can become a liability. Using pre-pandemic data from Cornell University, Weeden and Cornwell showed how co-enrollment in classes creates a “small world” network with high clustering, short path lengths, and multiple independent pathways connecting students. In this paper, we show how the structure of the enrollment network changed as Cornell, like many American colleges and universities, shifted to a hybrid instructional model with some courses online and others in person. Under this model, about half of students are disconnected from the in-person co-enrollment network. In this network, paths lengthened, the share of student pairs connected by three or fewer degrees of separation declined, and clustering increased, with a greater share of ties occurring between students in the same field. The small world became both less connected and more fragmented. (Corrected page proofs. Paper is forthcoming in Sociological Science.)

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The small-world network of global protests

Scientific Reports ◽

10.1038/s41598-021-98628-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Leonardo N. Ferreira ◽

Inho Hong ◽

Alex Rutherford ◽

Manuel Cebrian

Keyword(s):

Small World ◽

Data Sets ◽

Cascade Process ◽

Topological Feature ◽

Small World Network ◽

Temporal Network ◽

Temporal Networks ◽

Data Set ◽

The World ◽

Path Lengths

AbstractProtest diffusion is a cascade process that can spread over different regions of the planet. The way and the extension that this phenomenon can occur is still not properly understood. Here, we empirically investigate this question using protest data from GDELT and ICEWS, two of the most extensive and longest-running data sets freely available. We divide the globe into grid cells and construct a temporal network for each data set where nodes represent cells and links are established between nodes if their protest events co-occur. We show that the temporal networks are small-world, indicating that the cells are directly linked or separated by a few steps on average. Furthermore, the average path lengths are decreasing through the years, which suggests that the world is becoming “smaller”. The persistent temporal hubs present in both data sets indicate that protests can spread faster through the hubs. This topological feature is consistent with the hypothesis that protests can quickly diffuse from one region to any other part of the globe.

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Still a Small World? University Course Enrollment Networks before and during the COVID-19 Pandemic

Sociological Science ◽

10.15195/v8.a4 ◽

2021 ◽

Vol 8 ◽

pp. 73-82

Author(s):

Kim Weeden ◽

Benjamin Cornwell ◽

Barum Park

Keyword(s):

Small World ◽

Course Enrollment ◽

University Course

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Network assessment and modeling the management of an epidemic on a college campus with testing, contact tracing, and masking

PLoS ONE ◽

10.1371/journal.pone.0257052 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257052

Author(s):

Gregg Hartvigsen

Keyword(s):

Disease Transmission ◽

Liberal Arts ◽

Transmission Probability ◽

College Campus ◽

Contact Tracing ◽

Random Networks ◽

Course Enrollment ◽

Residential College ◽

Testing Frequency ◽

Path Lengths

There remains a great challenge to minimize the spread of epidemics, especially in high-density communities such as colleges and universities. This is particularly true on densely populated, residential college campuses. To construct class and residential networks data from a four-year, residential liberal arts college with 5539 students were obtained from SUNY College at Geneseo, a rural, residential, undergraduate institution in western NY, USA. Equal-sized random networks also were created for each day. Different levels of compliance with mask use (none to 100%), mask efficacy (50% to 100%), and testing frequency (daily, or every 2, 3, 7, 14, 28, or 105 days) were assessed. Tests were assumed to be only 90% accurate and positive results were used to isolate individuals. The effectiveness of contact tracing, and the effect of quarantining neighbors of infectious individuals, was tested. The structure of the college course enrollment and residence networks greatly influenced the dynamics of the epidemics, as compared to the random networks. In particular, average path lengths were longer in the college networks compared to random networks. Students in larger majors generally had shorter average path lengths than students in smaller majors. Average transitivity (clustering) was lower on days when students most frequently were in class (MWF). Degree distributions were generally large and right skewed, ranging from 0 to 719. Simulations began by inoculating twenty students (10 exposed and 10 infectious) with SARS-CoV-2 on the first day of the fall semester and ended once the disease was cleared. Transmission probability was calculated based on an R0 = 2.4. Without interventions epidemics resulted in most students becoming infected and lasted into the second semester. On average students in the college networks experienced fewer infections, shorter duration, and lower epidemic peaks when compared to the dynamics on equal-sized random networks. The most important factors in reducing case numbers were the proportion masking and the frequency of testing, followed by contact tracing and mask efficacy. The paper discusses further high-order interactions and other implications of non-pharmaceutical interventions for disease transmission on a residential college campus.

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Mobile agents' dynamic small-world network based on attention mechanism

2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS) ◽

10.23919/apnoms50412.2020.9237037 ◽

2020 ◽

Author(s):

Rong Xie

Keyword(s):

Mobile Agents ◽

Small World ◽

Attention Mechanism ◽

Small World Network

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Analysis of Oncogene Protein Structure Using Small World Network Concept

Current Bioinformatics ◽

10.2174/1574893614666191113143840 ◽

2020 ◽

Vol 15 (7) ◽

pp. 732-740

Author(s):

Neetu Kumari ◽

Anshul Verma

Keyword(s):

Amino Acids ◽

Protein Structure ◽

Degree Distribution ◽

Protein Structures ◽

Small World ◽

Extreme Condition ◽

Centrality Measures ◽

Small World Network ◽

Network Concept ◽

Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

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The structure of co-publications multilayer network

Computational Social Networks ◽

10.1186/s40649-021-00089-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Ghislain Romaric Meleu ◽

Paulin Yonta Melatagia

Keyword(s):

Power Law ◽

Degree Distribution ◽

Preferential Attachment ◽

Small World ◽

Generation Model ◽

Small World Network ◽

Power Law Distribution ◽

Multilayer Networks ◽

Multilayer Network ◽

Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

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Top Museums on Instagram

International Journal of Computational Methods in Heritage Science ◽

10.4018/ijcmhs.2019070102 ◽

2019 ◽

Vol 3 (2) ◽

pp. 18-42 ◽

Cited By ~ 1

Author(s):

Vasiliki G. Vrana ◽

Dimitrios A. Kydros ◽

Evangelos C. Kehris ◽

Anastasios-Ioannis T. Theocharidis ◽

George I. Kavavasilis

Keyword(s):

Social Network ◽

Network Analysis ◽

Small World ◽

Marketing Strategies ◽

The Other ◽

Small World Network ◽

Scale Free ◽

Centrality Metrics ◽

Photo Sharing ◽

Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

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A Small World Routing Protocol in Wireless Sensor Networks

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.474-476.828 ◽

2011 ◽

Vol 474-476 ◽

pp. 828-833

Author(s):

Wen Jun Xu ◽

Li Juan Sun ◽

Jian Guo ◽

Ru Chuan Wang

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Routing Protocol ◽

Data Transmission ◽

Small World ◽

Wireless Sensor ◽

Small World Network ◽

Average Delay ◽

Network Diameter ◽

Simulation Results

In order to reduce the average path length of the wireless sensor networks (WSNs) and save the energy, in this paper, the concept of the small world is introduced into the routing designs of WSNs. So a new small world routing protocol (SWRP) is proposed. By adding a few short cut links, which are confined to a fraction of the network diameter, we construct a small world network. Then the protocol finds paths through recurrent propagations of weak and strong links. The simulation results indicate that SWRP reduces the energy consumption effectively and the average delay of the data transmission, which leads to prolong the lifetime of both the nodes and the network.

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