The Small-World Network of College Classes: Implications for Epidemic Spread on a University Campus

The Small World Network of College Classes: Implications for Epidemic Spread on a University Campus

10.31235/osf.io/n5gw4 ◽

2020 ◽

Cited By ~ 1

Author(s):

Kim A Weeden ◽

Benjamin Cornwell

Keyword(s):

Liberal Arts ◽

Small World ◽

University Campus ◽

Epidemic Spread ◽

Small Worlds ◽

American University ◽

Gateway Courses ◽

Path Lengths ◽

College Classes ◽

Novel Coronavirus

To slow the spread of the novel coronavirus, many universities shifted to online instruction and now face the question of whether and how to resume in-person instruction. This article uses transcript data from a medium-sized American university to describe three enrollment networks that connect students through classes, and in the process create social conditions for the spread of infectious disease: an university-wide network, an undergraduate-only network, and a liberal arts college network. All three networks are “small worlds” characterized by high clustering, short average path lengths, and multiple independent paths connecting students. Students from different majors cluster together, but gateway courses and distributional requirements create cross-major integration. Connectivity declines when large courses of 100 students or more are removed from the network, as might be the case if some courses are taught online, but moderately sized courses must also be removed before less than half of student-pairs are connected in three steps and less than two-thirds in four steps. In all simulations, most students are connected through multiple independent paths. Hybrid models of instruction can reduce but not eliminate the potential for epidemic spread through the small worlds of course enrollments.

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Epidemics

Natural Complexity ◽

10.23943/princeton/9780691176840.003.0009 ◽

2017 ◽

Author(s):

Paul Charbonneau

Keyword(s):

Scale Invariance ◽

Small World ◽

Complex Nature ◽

Small World Network ◽

Epidemic Spread ◽

Fire Model ◽

Random Walkers ◽

Contagious Diseases ◽

Spread Model ◽

Forest Fire Model

This chapter examines the complex nature of the epidemic spread of contagious diseases. It first describes the model of epidemic spread constructed by adding random walks on a lattice to the forest-fire model before discussing the implementation of the epidemic “algorithm” using a minimal Python code. It then considers a representative simulation showing a time series of the number of infected and healthy random walkers, along with the behavior of the epidemic spread model and the dynamic self-organization of epidemic surges around a marginal infection rate of exactly unity. It also explores the scale invariance of a small-world network connecting twelve nodes. The chapter includes exercises and further computational explorations, along with a suggested list of materials for further reading.

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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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