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13 pages, 605 KiB

Open AccessArticle

Exploring Simulated Residential Spending Dynamics in Relation to Income Equality with the Entropy Trace of the Schelling Model

by Theordoros Panagiotakopoulos, George-Rafael Domenikos and Alexander V. Mantzaris

Mathematics 2022, 10(18), 3323; https://doi.org/10.3390/math10183323 - 13 Sep 2022

Viewed by 1716

Abstract

The Schelling model of segregation has provided researchers with a simple model to explore residential dynamics and their implications upon the spatial distribution of resident identities. Due to the simplicity of the model, many modifications and extensions have been produced to capture different [...] Read more.

The Schelling model of segregation has provided researchers with a simple model to explore residential dynamics and their implications upon the spatial distribution of resident identities. Due to the simplicity of the model, many modifications and extensions have been produced to capture different aspects of the decision process taken when residents change locations. Research has also involved examining different metrics for track segregation along the trace of the simulation states. Recent work has investigated monitoring the simulation by estimating the entropy of the states along the simulation, which offers a macroscopic perspective. Drawing inspiration from empirical studies which indicate that financial status can affect segregation, a dual dynamic for movements based on identity and financial capital has been produced so that the expenditure of a monetary value occurs during residential movements. Previous work has only considered a single approach for this dynamic and the results for different approaches are explored. The results show that the definition of the expenditure dynamic has a large effect on the entropy traces and financial homogeneity. The design choice provides insight for how the housing market can drive inequality or equality. Full article

(This article belongs to the Special Issue Feature Papers in Complex Networks and Their Applications)

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10 pages, 2096 KiB

Open AccessArticle

Introducing Tagasaurus, an Approach to Reduce Cognitive Fatigue from Long-Term Interface Usage When Storing Descriptions and Impressions from Photographs

by Alexander V. Mantzaris, Randyll Pandohie, Michael Hopwood, Phuong Pho, Dustin Ehling and Thomas G. Walker

Technologies 2021, 9(3), 45; https://doi.org/10.3390/technologies9030045 - 29 Jun 2021

Cited by 1 | Viewed by 2854

Abstract

Digital cameras and mobile phones have given people around the world the ability to take a large number of photos and store them on their computers. As these images serve the purpose of storing memories and bringing them to mind in the potentially [...] Read more.

Digital cameras and mobile phones have given people around the world the ability to take a large number of photos and store them on their computers. As these images serve the purpose of storing memories and bringing them to mind in the potentially far future, it is important to also store the impressions a user may have from them. Annotating these images can be a laborious process and the work here presents an application design and functioning implementation, which is openly available now, to ease the effort of this task. It also draws inspiration from interface developments of previous applications such as the Nokia Lifeblog and the Facebook user interface. A different mode of sentiment entry is provided where users interact with slider widgets rather than select a emoticon from a set to offer a more fine grained value. Special attention is made to avoid cognitive strain by avoiding nested tool selections. Full article

(This article belongs to the Special Issue Multimedia Indexing and Retrieval)

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12 pages, 1294 KiB

Open AccessArticle

Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling

by Amirarsalan Rajabi, Alexander V. Mantzaris, Ece C. Mutlu and Ozlem O. Garibay

Appl. Sci. 2021, 11(12), 5367; https://doi.org/10.3390/app11125367 - 9 Jun 2021

Cited by 13 | Viewed by 3713

Abstract

Governments, policy makers, and officials around the globe are working to mitigate the effects of the COVID-19 pandemic by making decisions that strive to save the most lives and impose the least economic costs. Making these decisions require comprehensive understanding of the dynamics [...] Read more.

Governments, policy makers, and officials around the globe are working to mitigate the effects of the COVID-19 pandemic by making decisions that strive to save the most lives and impose the least economic costs. Making these decisions require comprehensive understanding of the dynamics by which the disease spreads. In traditional epidemiological models, individuals do not adapt their contact behavior during an epidemic, yet adaptive behavior is well documented (i.e., fear-induced social distancing). In this work we revisit Epstein’s “coupled contagion dynamics of fear and disease” model in order to extend and adapt it to explore fear-driven behavioral adaptations and their impact on efforts to combat the COVID-19 pandemic. The inclusion of contact behavior adaptation endows the resulting model with a rich dynamics that under certain conditions reproduce endogenously multiple waves of infection. We show that the model provides an appropriate test bed for different containment strategies such as: testing with contact tracing and travel restrictions. The results show that while both strategies could result in flattening the epidemic curve and a significant reduction of the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions. Full article

(This article belongs to the Special Issue Epidemiology and Public Health in Applied Sciences)

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18 pages, 6762 KiB

Open AccessArticle

Exploring the Value of Nodes with Multicommunity Membership for Classification with Graph Convolutional Neural Networks

by Michael Hopwood, Phuong Pho and Alexander V. Mantzaris

Information 2021, 12(4), 170; https://doi.org/10.3390/info12040170 - 15 Apr 2021

Cited by 3 | Viewed by 3876

Abstract

Sampling is an important step in the machine learning process because it prioritizes samples that help the model best summarize the important concepts required for the task at hand. The process of determining the best sampling method has been rarely studied in the [...] Read more.

Sampling is an important step in the machine learning process because it prioritizes samples that help the model best summarize the important concepts required for the task at hand. The process of determining the best sampling method has been rarely studied in the context of graph neural networks. In this paper, we evaluate multiple sampling methods (i.e., ascending and descending) that sample based off different definitions of centrality (i.e., Voterank, Pagerank, degree) to observe its relation with network topology. We find that no sampling method is superior across all network topologies. Additionally, we find situations where ascending sampling provides better classification scores, showing the strength of weak ties. Two strategies are then created to predict the best sampling method, one that observes the homogeneous connectivity of the nodes, and one that observes the network topology. In both methods, we are able to evaluate the best sampling direction consistently. Full article

(This article belongs to the Section Artificial Intelligence)

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22 pages, 1340 KiB

Open AccessArticle

Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks

by Neda H. Bidoki, Alexander V. Mantzaris and Gita Sukthankar

Mach. Learn. Knowl. Extr. 2020, 2(2), 125-146; https://doi.org/10.3390/make2020008 - 21 May 2020

Cited by 2 | Viewed by 4093

Abstract

This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. This is done [...] Read more.

This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. This is done by applying the methodological framework of the Simplified Graph Convolutional Neural Network (SGC) to two academic publication datasets: Cora and Citeseer. The performance of SGC is compared to the original Graph Convolutional Network (GCN) framework. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of SGC. When removal is based on a more localized selection of nodes, augmenting the network with both strong-ties and weak-ties provides a benefit, indicating that SGC successfully leverages local information of network nodes. Full article

(This article belongs to the Section Network)

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13 pages, 658 KiB

Open AccessArticle

An LSTM Model for Predicting Cross-Platform Bursts of Social Media Activity

by Neda Hajiakhoond Bidoki, Alexander V. Mantzaris and Gita Sukthankar

Information 2019, 10(12), 394; https://doi.org/10.3390/info10120394 - 14 Dec 2019

Cited by 9 | Viewed by 4633

Abstract

Burst analysis and prediction is a fundamental problem in social network analysis, since user activities have been shown to have an intrinsically bursty nature. Bursts may also be a signal of topics that are of growing real-world interest. Since bursts can be caused [...] Read more.

Burst analysis and prediction is a fundamental problem in social network analysis, since user activities have been shown to have an intrinsically bursty nature. Bursts may also be a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross platform social media data may be valuable for predicting bursts within a single social media platform. A Long-Short-Term-Memory (LSTM) model is proposed in order to capture the temporal dependencies and associations based upon activity information. The data used to test the model was collected from Twitter, Github, and Reddit. Our results show that the LSTM based model is able to leverage the complex cross-platform dynamics to predict bursts. In situations where information gathering from platforms of concern is not possible the learned model can provide a prediction for whether bursts on another platform can be expected. Full article

(This article belongs to the Special Issue Advances in Social Media Analysis)

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18 pages, 1465 KiB

Open AccessArticle

Exploring How Homophily and Accessibility Can Facilitate Polarization in Social Networks

by Cameron E. Taylor, Alexander V. Mantzaris and Ivan Garibay

Information 2018, 9(12), 325; https://doi.org/10.3390/info9120325 - 14 Dec 2018

Cited by 13 | Viewed by 5169

Abstract

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants on topics surrounding politics, climate, the economy and other areas where an agreement is [...] Read more.

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants on topics surrounding politics, climate, the economy and other areas where an agreement is required. This work investigates into greater depth a type of model that can produce ideological segregation as a result of polarization depending on the strength of homophily and the ability of users to access similar minded individuals. Whether increased access can induce larger amounts of societal separation is important to investigate, and this work sheds further insight into the phenomenon. Center to the hypothesis of homophilic alignments in friendship generation is that of a discussion group or community. These are modeled and the investigation into their effect on the dynamics of polarization is presented. The social implications demonstrate that initial phases of an ideological exchange can result in increased polarization, although a consensus in the long run is expected and that the separation between groups is amplified when groups are constructed with ideological homophilic preferences. Full article

(This article belongs to the Special Issue Information Diffusion in Social Networks)

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13 pages, 394 KiB

Open AccessArticle

Examining the Schelling Model Simulation through an Estimation of Its Entropy

by Alexander V. Mantzaris, John A. Marich and Tristin W. Halfman

Entropy 2018, 20(9), 623; https://doi.org/10.3390/e20090623 - 21 Aug 2018

Cited by 6 | Viewed by 4887

Abstract

The Schelling model of segregation allows for a general description of residential movements in an environment modeled by a lattice. The key factor is that occupants change positions until they are surrounded by a designated minimum number of similarly labeled residents. An analogy [...] Read more.

The Schelling model of segregation allows for a general description of residential movements in an environment modeled by a lattice. The key factor is that occupants change positions until they are surrounded by a designated minimum number of similarly labeled residents. An analogy to the Ising model has been made in previous research, primarily due the assumption of state changes being dependent upon the adjacent cell positions. This allows for concepts produced in statistical mechanics to be applied to the Schelling model. Here is presented a methodology to estimate the entropy of the model for different states of the simulation. A Monte Carlo estimate is obtained for the set of macrostates defined as the different aggregate homogeneity satisfaction values across all residents, which allows for the entropy value to be produced for each state. This produces a trace of the estimated entropy value for the states of the lattice configurations to be displayed with each iteration. The results show that the initial random placements of residents have larger entropy values than the final states of the simulation when the overall homogeneity of the residential locality is increased. Full article

(This article belongs to the Section Statistical Physics)

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