Detection of Semantic Duplicates in Temporal Domains Using Directed Acyclic Graphs

We demonstrate the feasibility of a generalized technique for semantic deduplication in temporal data domains using graph-based representations of data records. Structured data records with multiple timestamp attributes per record may be represented as a directed graph where the nodes represent the events and the edges represent event sequences. Edge weights are based on elapsed time between connecting nodes. In comparing two records, we may merge these directed graphs and determine a representative directed acyclic graph (DAG) inclusive of a subset of nodes and edges that maintain the transitive weights of the original graphs. This DAG may then be evaluated by weighting elapsed time equivalences between records at each node and measuring the fraction of nodes represented in the DAG versus the union of nodes between the records being compared. With this information, we establish a duplication score and use a specified threshold requirement to assert duplication. This method is referred to as Temporal Deduplication using Directed Acyclic Graphs (TD:DAG). TD:DAG significantly outperformed established ASNM and ASNM+LCS methods for datasets rep-resenting two disparate domains, COVID-19 government policy data and PlayStation Network (PSN) trophy data. TD:DAG produced highly effective and comparable F1 scores of 0.960 and 0.972 for the two datasets, respectively, versus 0.864/0.938 for ASNM+LCS and 0.817/0.708 for ASNM. © 2023 IEEE.

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

An acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. The proposed method leverages the concept of topological layer to facilitate the DAG learning, and its theoretical justification in terms of exact DAG recovery is also established under mild conditions. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes can also be consistently established. The established asymptotic DAG recovery is in sharp contrast to that of many existing learning methods assuming parental faithfulness or ordered noise variances. The advantage of the proposed method is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19. ©2022 Ruixuan Zhao, Xin He, and Junhui Wang.

Graph Convolutional Network-Based Rumor Blocking on Social Networks

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms. IEEE

Modeling of Critically Ill Patient Pathways to Support Intensive Care Delivery

The COVID-19 pandemic has exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. New methods and strategies to facilitate timely and accurate interventions are needed. A virtual counterpart (digital twin) to critically ill patients would allow bedside providers to visualize how the organ systems interact to cause a clinical effect, offering them the opportunity to evaluate the effect of a specific intervention on a virtual patient before exposing an actual patient to potential harm. This work aims at developing a digital simulation that models the clinical pathway of critically ill patients. Using the mixed-methods approach with the support of multiprofessional clinical experts, we first identify the causal and associative relationships between organ systems, medical conditions, clinical markers, and interventions. We record these relationships as structured expert rules, depict them in a directed acyclic graph (DAG) format, and store them in a graph database (Neo4j). These structured expert rules are subsequently utilized to drive a simulation application that enables users to simulate the state trajectory of critically ill patients over a given simulated time period to test the impact of different interventions on patient outcomes. This simulation model will be the engine driving a future digital twin prototype, which will be used as an educational tool for medical students, and as a bedside decision support tool to enable clinicians to make faster and more informed treatment decisions. IEEE

Causal Graph and Social Network Analysis for the Spread of COVID-19 from Self-reported Indicator Data

Dynamic Bayesian Network (DBN) is an useful tool to learn the causal inference and social network of random variables. In this article, we analyze the correlations between the spread of coronavirus (COVID-19) and certain self-reported COVID-19 indicators in the United States, and then adopt DBN model with search and score-based approach to analyze and interpret the causal relationships and social network between these variables by learning the structure of the Directed Acyclic Graph from the model. We explore the change of causality among fifty states during the pandemic of COVID-19 in the year of 2020 and interpret the root cause for changes and trends. We concentrate on five worst states with COVID-19 and then extended our studies to all states by comparing the causal relationships and analyzing the patterns of DAG. © 2021 IEEE.

Complementary Blockchain-Based Privacy Protection for Covid-19 Contact Tracing

Fighting any pandemic outbreak begins with health authorities already behind time. They are required to locate, isolate and treat infected individuals while tracking possibly infected ones. In the case of Covid-19, the high reproductive number of the virus necessitates that all contacts of an infected individual be found within the shortest possible time to slow the rate of spreading. This presents multiple challenges because of the highly invasive nature of tracing activities which demand the mobility history of patients. Patients may be unwilling to cooperate or may be unable to communicate if the infection has advanced to the point where critical care is necessary. To speedily locate contacts of an infected individual, we propose using communications data logs from telecommunications operators. We employ a modified directed graph to determine which other individuals have been in close proximity to an infected individual in a specific frame of time. We then generate a contact graph and place it in a secure offline storage platform. We employ Smart Contracts to control access to the data while the blockchain keeps records of the provenance of all data and transactions. We find this method of conducting the contact tracing and protecting the resulting data more secure and pliant to the privacy laws that regulate the handling of sensitive personal data. © 2021 IEEE.

Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

Modeling the spatiotemporal nature of the spread of infectious diseases can provide useful intuition in understanding the time-varying aspect of the disease spread and the underlying complex spatial dependency observed in people's mobility patterns. Besides, the county level multiple related time series information can be leveraged to make a forecast on an individual time series. Adding to this challenge is the fact that real-time data often deviates from the unimodal Gaussian distribution assumption and may show some complex mixed patterns. Motivated by this, we develop a deep learning-based time-series model for probabilistic forecasting called Auto-regressive Mixed Density Dynamic Diffusion Network (ARM3Dnet), which considers both people's mobility and disease spread as a diffusion process on a dynamic directed graph. The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the realtime data while learning from multiple related time series. We show that our model, when trained with the best combination of dynamic covariate features and mixture components, can outperform both traditional statistical and deep learning models in forecasting the number of Covid-19 deaths and cases at the county level in the United States. © 2021 ACM.

A city cluster risk-based approach for Sars-CoV-2 and isolation barriers based on anonymized mobile phone users' location data.

Given the recent outbreak of Sars-CoV-2, several countries started to seek different strategies to control contamination and minimize fatalities, which are usually the primary objectives for all strategies. Secondary objectives are related to economic factors, therefore ensuring that society would be able is to keep its essential activities and avoid supply disruptions. This paper presents an application of anonymized mobile phone users' location data to estimate population flow amongst cities with an origin-destination matrix. The work includes a clustering analysis of cities, which may enable policymakers (and epidemiologists) to develop public policies giving the appropriate consideration for each set of cities within a Province or State. Risk measures are included to analyze the severity of the spread among the clusters, which can be ranked. Then, intelligence can be obtained from the analysis, and some clusters could be isolated to avoid contagion while keeping their economic activities. Therefore, this analysis is reproducible for other states of Brazil and other countries and can be adapted for districts within a city, especially considering the possibility of a second wave COVID-19 pandemic.