scholarly journals Correlation coefficient analysis: centrality vs. maximal clique size for complex real-world network graphs

2016 ◽  
Vol 1 (1) ◽  
pp. 3 ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Correlation Coefficient ◽  
Real World ◽  
Maximal Clique

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2019 ◽  
pp. 1183-1198
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

The authors present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. They consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC), and betweenness centrality (BWC). They define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. The authors observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. They observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2018 ◽  
pp. 6507-6521
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Computer Vision Based Asset Surveillance for Smart Buildings

2020 ◽  
Vol 17 (1) ◽  
pp. 456-463
Author(s):  
K. S. Gautam ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Computer Vision ◽  
Correlation Coefficient ◽  
Real World ◽  
Vision System ◽  
Region Of Interest ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Ip Camera ◽  
Pros And Cons ◽  
Real World Problems

Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.

scholarly journals Correlation Analysis between Maximal Clique Size and Centrality Metrics for Random Networks and Scale-Free Networks

2016 ◽  
Vol 9 (2) ◽  
pp. 41 ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Correlation Coefficient ◽  
Random Network ◽  
Maximal Clique ◽  
Random Networks ◽  
Font Size ◽  
Scale Free ◽  
Node Centrality ◽  
Centrality Metrics ◽  
Scale Free Networks ◽  
The Impact

<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The high-level contribution of this paper is a comprehensive analysis of the correlation levels between node centrality (a computationally light-weight metric) and maximal clique size (a computationally hard metric) in random network and scale-free network graphs generated respectively from the well-known Erdos-Renyi (ER) and Barabasi-Albert (BA) models. We use three well-known measures for evaluating the level of correlation: Product-moment based Pearson's correlation coefficient, Rank-based Spearman's correlation coefficient and Concordance-based Kendall's correlation coefficient. For each of the several variants of the theoretical graphs generated from the ER and BA models, we compute the above three correlation coefficient values between the maximal clique size for a node (maximum size of the clique the node is part of) and each of the four prominent node centrality metrics (degree, eigenvector, betweenness and closeness). We also explore the impact of the operating parameters of the theoretical models for generating random networks and scale-free networks on the correlation between maximal clique size and the centrality metrics.</span></p>

scholarly journals Smartphone-Based VO2max Measurement With Heart Snapshot in Clinical and Real-world Settings With a Diverse Population: Validation Study

10.2196/26006 ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. e26006
Author(s):  
Dan E Webster ◽  
Meghasyam Tummalacherla ◽  
Michael Higgins ◽  
David Wing ◽  
Euan Ashley ◽  
...  
Keyword(s):  
Heart Rate ◽  
Correlation Coefficient ◽  
Intraclass Correlation Coefficient ◽  
Real World ◽  
Gold Standard ◽  
Clinical Care ◽  
Skin Pigmentation ◽  
Intraclass Correlation ◽  
Step Test ◽  
Validation Data

Background Maximal oxygen consumption (VO2max) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VO2max is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff. Objective To overcome the limitations of clinical VO2max measurement, we aim to develop a digital VO2max estimation protocol that can be self-administered remotely using only the sensors within a smartphone. We also aim to validate this measure within a broadly representative population across a spectrum of smartphone devices. Methods Two smartphone-based VO2max estimation protocols were developed: a 12-minute run test (12-MRT) based on distance measured by GPS and a 3-minute step test (3-MST) based on heart rate recovery measured by a camera. In a 101-person cohort, balanced across age deciles and sex, participants completed a gold standard treadmill-based VO2max measurement, two silver standard clinical protocols, and the smartphone-based 12-MRT and 3-MST protocols in the clinic and at home. In a separate 120-participant cohort, the video-based heart rate measurement underlying the 3-MST was measured for accuracy in individuals across the spectrum skin tones while using 8 different smartphones ranging in cost from US $99 to US $999. Results When compared with gold standard VO2max testing, Lin concordance was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. However, in remote settings, the 12-MRT was significantly less concordant with the gold standard (pc=0.25) compared with the 3-MST (pc=0.61), although both had high test-retest reliability (12-MRT intraclass correlation coefficient=0.88; 3-MST intraclass correlation coefficient=0.86). On the basis of the finding that 3-MST concordance was generalizable to remote settings whereas 12-MRT was not, the video-based heart rate measure within the 3-MST was selected for further investigation. Heart rate measurements in any of the combinations of the six Fitzpatrick skin tones and 8 smartphones resulted in a concordance of pc≥0.81. Performance did not correlate with device cost, with all phones selling under US $200 performing better than pc>0.92. Conclusions These findings demonstrate the importance of validating mobile health measures in the real world across a diverse cohort and spectrum of hardware. The 3-MST protocol, termed as heart snapshot, measured VO2max with similar accuracy to supervised in-clinic tests such as the Tecumseh (pc=0.94) protocol, while also generalizing to remote and unsupervised measurements. Heart snapshot measurements demonstrated fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between various iOS and Android phone configurations. This software is freely available for all validation data and analysis code.

Spatiotemporal modeling with SimBioSys TumorScope to predict chemotherapeutic response in breast tumor microenvironments.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e12656-e12656
Author(s):  
Chaitanya K Mamillapalli ◽  
Timothy K Markwell ◽  
Jason K Ellis ◽  
John Pfiefer ◽  
Tushar Pandey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Correlation Coefficient ◽  
Real World ◽  
Tumor Volume ◽  
Pearson Correlation ◽  
Tumor Therapy ◽  
Standard Of Care ◽  
Surrounding Tissue ◽  
Pearson Correlation Coefficient ◽  
Clinical Records

e12656 Background: Predictive models of the efficacy of different tumor therapies will provide significant enhancements to current standard of care practices. Predicting a given tumor’s growth and treatment response, however, is an intricate process that requires not only an understanding of the tumor's intrinsic characteristics, but also spatial- and temporal-resolved tumor shape descriptions, surrounding tissue dynamics, and a complete account for the milieu of diffusible molecules that drive tumor behaviors and interactions. Here we report an ongoing retrospective study designed to validate SimBioSys TumorScope as a computational tumor therapy prediction model in a real-world clinical setting. Methods: Fully-deidentified and HIPAA-compliant data were assessed from real-world clinical records and cases. Subjects comprised early stage breast cancer patients who were treated with neoadjuvant chemotherapy (NACT) and subsequent surgical resection. Data fields included imaging data, biomarker status, tumor sizing, demographic data, digital pathology, and genetic lab test data. Half of the data, including all data fields, were used as a training dataset for TumorScope. The second half of the data, with blinded diagnoses and results, will be used to test TumorScope’s prediction accuracy. Simulations will be initialized with pre-treatment MRI data and processed through the entirety of each patient's specified treatment regimen. Predicted tumor volume and longest dimension will be compared against measured values at several time-points after therapy initiation. Overall accuracy will be statistically assessed by the Pearson correlation coefficient between predicted and measured tumor volume and longest dimension at each time-point, as well as their root-mean-squared-errors. Results: Final statistical analysis is currently underway. Thus far, SimBioSys TumorScope has trended high accuracy levels with the non-blinded “training” cohort just as it has in previous database studies, with a Pearson correlation coefficient greater than 0.94. Conclusions: SimBioSys TumorScope for Breast Cancer accurately predicts patient NACT responses via spatio-temporal modeling of drug and nutrient perfusion, metabolic behavior, and the physio-chemical interactions between surrounding tissues. Future prospective studies may assess TumorScope’s capacity to support efficient patient treatments and enhance overall standard of care.

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