The Application of PageRank model in urban epidemic prevention and control

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.

A method of mechanism analysis about a traditional chinese medicine prescription on a disease based on PageRank algorithm and network pharmacology

Network pharmacology is a method to study the mechanism of a Traditional Chinese Medicine (TCM) prescription on a disease. However, most articles using network pharmacology to study the mechanism did not combine the weight information of herbs, the weight information of targets of disease, and the interaction information between targets together. We propose a method, network pharmacology combined with two iterations of PageRank algorithm, to make use of these information. It takes prescription-disease system as a whole, calculates PageRank score of targets in the prescription-disease system, which means an importance in the system, and the score is used to rank the analysis results of GO and KEGG pathway which help us to analyze the mechanism of a prescription on a disease. At last, we use two prescription-disease pairs which have been proved effectiveness in clinical trials: Qingfei Paidu Decoction on COVID-19, and FuFang DanShen Diwan on Coronary Heart Disease, and find that the results of our method are consistent with some results of clinical trials.Copyright © 2021

Using Social Interactions to Redefine Vaccination Strategy

The pandemics are believed to change the human perception and significantly affect the socio-economical, environmental and psychological outlook of affected people. The recent Covid-19 pandemic has challenged the state of art healthcare systems and has put modern day technology driven healthcare system to a task. While the doctors, biotechnologist, epidemiologist and technologist put their heart in, to model and study the impact of Covid-19;the researchers were tirelessly working on identifying a vaccine that can efficiently put an end to the pandemic. The mass vaccination has always seemed a solution to communicable diseases, pandemics and endemics. The authors believe an efficient vaccination strategy / model is needed to reach the major population in least possible time. It will facilitate to reach the goal of mass vaccination and decrease the spread of virus. The paper presents a PageRank based vaccination model that utilizes the depth first search to traverse a social graph that proves to converge faster than most widely used Random Walk. The idea is to prioritize the vaccination of the most connected individual who is more likely to be a victim or be a super-spreader. The paper also studies the hesitation and acceptance of vaccination among various communities. © 2022 IEEE.

On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

What happened on social media during the recent pandemic? Who was the opinion leader of the conversations? Who influenced whom? Were they medical doctors, ordinary people, scientific experts? Did health institutions play an important role in informing and updating citizens? Identifying opinion leaders within social platforms is of particular importance and, in this paper, we introduce the idea of a time sensitive interaction graph to identify opinion leaders within Twitter conversations. To evaluate our proposal, we focused on all the tweets posted on Twitter in the period 2020-21 and we considered just the ones that were Italian-written and were related to COVID-19. After mapping these tweets into the graph, we applied the PageRank algorithm to extract the opinion leaders of these conversations. Results show that our approach is effective in identifying opinion leaders and therefore it might be used to monitor the role that specific accounts (i.e., health authorities, politicians, city administrators) have within specific conversations. © 2022 ACM.

Networks of causal relationships in the U.S. stock market

We consider a network-based framework for studying causal relationships in financial markets and demonstrate this approach by applying it to the entire U.S. stock market. Directed networks (referred to as "causal market graphs") are constructed based on publicly available stock prices time series data during 2001-2020, using Granger causality as a measure of pairwise causal relationships between all stocks. We consider the dynamics of structural properties of the constructed network snapshots, group stocks into network-based clusters, as well as identify the most "influential" market sectors via the PageRank algorithm. Interestingly, we observed drastic changes in the considered network characteristics in the years that corresponded to significant global-scale events, most notably, the financial crisis of 2008 and the COVID-19 pandemic of 2020.

Analysis of the Effect of Covid-19 on the Floating Population of Seoul Using the PageRank Algorithm

This study aims to analyze the effects of Covid-19 on the floating population of Seoul, based on population influx/outflux data from January-June, 2019 and January- June, 2020. The datasets are partitioned into their respective administrative districts. Moreover, to understand the effects of Covid-19, the PageRank algorithm is employed to analyze and identify the districts with the most population influx as well as the changes in population movement in Seoul between 2019 to 2020.

Finding Novel Links in COVID-19 Knowledge Graph Using Graph Embedding Techniques

With the rapid proliferation of scientific literature, it has become increasingly impossible for researchers to keep up with all published papers, especially in the biomedical fields with thousands of citations indexed every day. This has created a demand for algorithms to assist in literature search and discovery. A particular case is the literature related to SARS-CoV-2 where a large volume of papers was generated in a short span. As part of the 2021 Smoky Mountains Data Challenge, a COVID-19 knowledge graph constructed using links between concepts and papers from PubMed, Semantic MEDLINE, and CORD-19, was provided for analysis and knowledge mining. In this paper, we analyze this COVID-19 knowledge graph and implement various algorithms to predict as-yet-undiscovered links between concepts, using methods of embedding concepts in Euclidean space followed by link prediction using machine learning algorithms. Three embedding techniques: the Large-scale Information Network Embedding (LINE), the High-Order Proximity-preserved Embedding (HOPE) and the Structural Deep Network Embedding (SDNE) are implemented in conjunction with three machine learning algorithms (logistic regression, random forests, and feed forward neural-networks). We also implement GraphSAGE, another framework for inductive representation on large graphs. Among the methods, we observed that SDNE in conjunction with feed-forward neural network performed the best with an F1 score of 88.0% followed by GraphSAGE with F1 score of 86.3%. The predicted links are ranked using PageRank product to assess the relative importance of predictions. Finally, we visualize the knowledge graphs and predictions to gain insight into the structure of the graph. © 2022, Springer Nature Switzerland AG.

An Awareness to Multisystem Inflammatory Syndrome (MIS-C) using Social Networks

The study about the health conditions on patients post-Covid-health is exhibiting alarming statistics. The children under the age of eighteen constituted the only 8.5% of the total affected population. A month into the pandemic, a novel multisystem inflammatory syndrome in children (MIS-C) emerged. MIS-C is found to be serious and even life-threatening. This demands introducing awareness and knowledge among the parents regarding the clinical signs and symptoms of MIS-C. In this paper, we identify the need and method to spread awareness of post-COVID-19 health care to society through social media. In this paper, we identify the need and method to spread awareness of MIS-C to parental society through social media. © 2021 IEEE.

COVID-19's Impact on International Trade.

We analyze how the COVID-19 pandemic affected the trade of products between countries. With this aim, using the United Nations Comtrade database, we perform a Google matrix analysis of the multiproduct World Trade Network (WTN) for the years 2018-2020, comprising the emergence of the COVID-19 as a global pandemic. The applied algorithms-PageRank, CheiRank and the reduced Google matrix-take into account the multiplicity of the WTN links, providing new insights into international trade compared to the usual import-export analysis. These complex networks analysis algorithms establish new rankings and trade balances of countries and products considering all countries on equal grounds, independent of their wealth, and every product on the basis of its relative exchanged volumes. In comparison with the pre-COVID-19 period, significant changes in these metrics occurred for the year 2020, highlighting a major rewiring of the international trade flows induced by the COVID-19 pandemic crisis. We define a new PageRank-CheiRank product trade balance, either export or import-oriented, which is significantly perturbed by the pandemic.

Research on the Evaluation of Program Influence Based on PageRank Algorithm

The paper introduces the complex network theory into the program influence evaluation system. We collect relevant data of the variety shows on Tencent Video Platform from 2016-2021 and establish the program comprehensive influence algorithm program. The comprehensive influence consists of two direct and indirect parts, the direct influence is calculated from the total view counts, the comments in the latest issue, and the fans of the official program account. The indirect influence is calculated by PageRank, LeaderRank and TimedPageRank through the program page. The results obtained by the algorithm shows that the output of highly influential variety shows has increased in recent years. Even though COVID-19 in 2020 reduced the total production of variety shows, it still produces a relatively high proportion of influential programs. High influence programs are mainly 'Develop' and 'Game' programs, 'Cultural' programs rank low. Inspire our need to strengthen the content production and value concept guidance of the variety show market, and deepen the content innovation and reform of the 'Cultural' programs themselves. © 2021 IEEE.

Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach.

BACKGROUND: From February 2020, both urban and rural Ireland witnessed the rapid proliferation of the COVID-19 disease throughout its counties. During this period, the national COVID-19 responses included stay-at-home directives issued by the state, subject to varying levels of enforcement. METHODS: In this paper, we present a new method to assess and rank the causes of Ireland COVID-19 deaths as it relates to mobility activities within each county provided by Google while taking into consideration the epidemiological confirmed positive cases reported per county. We used a network structure and rank propagation modelling approach using Personalised PageRank to reveal the importance of each mobility category linked to cases and deaths. Then a novel feature-selection method using relative prominent factors finds important features related to each county's death. Finally, we clustered the counties based on features selected with the network results using a customised network clustering algorithm for the research problem. FINDINGS: Our analysis reveals that the most important mobility trend categories that exhibit the strongest association to COVID-19 cases and deaths include retail and recreation and workplaces. This is the first time a network structure and rank propagation modelling approach has been used to link COVID-19 data to mobility patterns. The infection determinants landscape illustrated by the network results aligns soundly with county socio-economic and demographic features. The novel feature selection and clustering method presented clusters useful to policymakers, managers of the health sector, politicians and even sociologists. Finally, each county has a different impact on the national total.

Studying leaders & their concerns using online social media during the times of crisis - A COVID case study.

Online social media (OSM) has emerged as a prominent platform for debate on a wide range of issues. Even celebrities and public figures often share their opinions on a variety of topics through OSM platforms. One such subject that has gained a lot of coverage on Twitter is the Novel Coronavirus, officially known as COVID-19, which has become a pandemic and has sparked a crisis in human history. In this study, we examine 29 million tweets over three months to study highly influential users, whom we refer to as leaders. We recognize these leaders through social network techniques and analyse their tweets using text analysis. Using a community detection algorithm, we categorize these leaders into four clusters: research, news, health, and politics, with each cluster containing Twitter handles (accounts) of individual users or organizations. e.g., the health cluster includes the World Health Organization (@WHO), the Director-General of WHO (@DrTedros), and so on. The emotion analysis reveals that (i) all clusters show an equal amount of fear in their tweets, (ii) research and news clusters display more sadness than others, and (iii) health and politics clusters are attempting to win public trust. According to the text analysis, the (i) research cluster is more concerned with recognizing symptoms and the development of vaccination; (ii) news and politics clusters are mostly concerned with travel. We then show that we can use our findings to classify tweets into clusters with a score of 96% AUC ROC.