ABSTRACT
Knowledge graphs are being used for the detection of money laundering, insurance fraud, and other suspicious activities. Some recent work demonstrated how knowledge graphs are being used to study the impact of the COVID-19 outbreak on the economy. The fact that knowledge graphs are being used in more and more interdisciplinary problems calls for a reliable source of interdisciplinary knowledge. In this paper, we study the integration of knowledge graphs in the domains of economics, banking, and finance. Our integrated knowledge graph has over 610K nodes and 1.7 million edges. By performing statistical and graph-theoretical analysis, we demonstrate how the integration results in more entities with richer information. Its quality was examined by analyzing the subgraphs of the identity links and (pseudo-)transitive relations. Finally, we study the sources of error, and their refinement and discuss the benefit of our integrated graph. © 2022 Copyright for this paper by its authors.
ABSTRACT
A graph analysis on the tweets and users networks from a set of curated news was done to study the existing difference in communication patterns between legitimate and misinformation news. Our findings suggest there is no difference in the influence of misinformation and legitimate news but misinformation news tend to be more shared and present than legitimate news, meaning that while misinformation tweets do not have more influence, their authors are more prolific. Misinformation reach wider audience even if the tweets, individually, are not more influential. A subsequent qualitative analysis on the users reveal that there is also influence of misinformation spreading in Spain from other Spanish speaking countries. © 2021, Springer Nature Switzerland AG.
ABSTRACT
One of the most significant and rapidly developing works in the field of data analysis is information flow management. Within the analysis targeted and stochastic dissemination patterns are studied. The solving of such problems is relevant due to the global growth in the amount of information and its availability for a wide range of users. The paper presents a study of dissemination of information messages in open networks on the example of COVID-19. The study was conducted with the use of visual analytics. Informational messages from the largest world and Russian information services, social networks and instant messengers were used as sources of information. Due to the large amount of information on the topic, the authors proposed a pattern of the wave-like dissemination of information on the example of topic clusters on the connection of COVID-19, hydroxychloroquine and 5G. The developed methods can be scaled up to analyze information events of various topics. © 2021 Copyright for this paper by its authors.
ABSTRACT
COVID-19 pandemic created a shockwave that can be felt across every sphere of society and the environment. The disease grew from a local event to a pandemic and the impacts are not the same everywhere. Therefore, there is a need to characterise the impact at different levels to ensure that initiatives to cushion the impacts are well-targeted. This study utilised graph analysis to examine the network attributes of Facebook Users’ mobility during the pandemic in comparison with the baseline period before the pandemic to characterise the economic impact. Movement data were collated from the Facebook Data for Good platform while economic output and census data were collated from the National Bureau of Statistics. The result shows that economic output and baseline network attributes have a positive and highly significant relationship (P<0.01). Nodal efficiency was statistically different across the crisis and baseline periods while betweenness showed no difference. The sum of the difference from baseline values identified two extremes – Lagos State (most negatively impacted) and Kwara State (most positively impacted). The States adopted varying measures to combat the disease, these variations also emerged in the graph analysis results. Economic output from each State is related to its centrality and efficiency. It is therefore plausible that changes in network attributes will bring commensurate changes in the economic output of each State. States with high centrality and betweenness values had a greater decline in their network attributes. The study provided an insight into one aspect of the extent of the economic impact of the COVID-19 on each State. We recommend more investigations into the inclusion of local interactions in capturing intra-state movements and changes in modelling economic output. © 2021, Comenius University in Bratislava. All rights reserved.
ABSTRACT
This study focused on the interaction of demographics and well-being. Diener's subjective well-being (SWB) was successfully validated with Exploratory Graph Analysis and Confirmatory Factor Analysis to track well-being differences of the COVID-19 quarantined individuals. Six tree-based Machine Learning models were trained to classify top 25% SWB scorers during COVID-19 quarantine, after data-splitting (train 70%, test 30%). The model input variables were demographics, to avoid overlapping of inputs-outputs. A 10-fold cross-validation method (70%-30%) was then implemented in the training session to select the optimal Machine Learning model among the six tested. A CART classification was the optimal algorithm (Train-Accuracy = 0.77, Test-Accuracy = 0.75). A clean, three-node tree suggested that if someone spends time on perceived creative activities during the COVID-19 quarantine, under clearly described conditions, he/she had high probabilities to be a top subjective well-being scorer. The key importance of creative activities was subsequently cross-validated with three different model configurations: (1) a different tree-based model (Test-Accuracy =0.75); (2) a different operationalization of subjective well-being (Test-Accuracy =0.75) and (3) a different construct (depression; Test-Accuracy =0.73). This is an integrative approach to study individual differences in subjective well-being, bridging Exploratory Graph Analysis and Machine Learning in a single research cycle with multiples cross-validations.