Top-K Nodes Identification in Big Networks Based on Topology and Activity Analysis

Graphs and Networks have been the most researched topics with applications ranging from theoretical to practical fields, such as social media, genetics, and education. In many competitive environments, the most productive activities may be interacting with high-profile people, reading a much-cited article, or researching a wide range of fields such as the study on highly connected proteins. This thesis proposes two methods to deal with top-K nodes identification: centrality-based and activity-based methods for identifying top-K nodes. The first method is based on the topological structure of the network and uses the centrality measure called Katz Centrality; a path based ranking measure that calculates the local influence of a node as well as its global influence. It starts by filtering out the top-K nodes from a pool of network data using Katz Centrality. By providing a means to filter out unnecessary nodes based on their centrality values, one can focus more on the most important nodes. The proposed method was applied to various network data and the results showed how different parameter values lead to different numbers of top-K nodes. The second method incorporates the theory of heat diffusion. Each node in the network can act as the source of heat. The amount of heat diffused or received by the node depends on the number of activities it performs. There are two types of activities: Interactive and Non-Interactive. Interactive activities could be likes, comments, and shares whereas posting a status, tweets or pictures could be the examples of non-interactive activities. We applied these proposed methods on Instagram network data and compared the results with the other similar algorithms. The experiment results showed that our activity-based approach is much faster and accurate than the existing methods. Images referenced in this thesis are included in the supplementary files

Identification of top-K nodes in large networks using Katz centrality

Abstract Network theory concepts form the core of algorithms that are designed to uncover valuable insights from various datasets. Especially, network centrality measures such as Eigenvector centrality, Katz centrality, PageRank centrality etc., are used in retrieving top-K viral information propagators in social networks,while web page ranking in efficient information retrieval, etc. In this paper, we propose a novel method for identifying top-K viral information propagators from a reduced search space. Our algorithm computes the Katz centrality and Local average centrality values of each node and tests the values against two threshold (constraints) values. Only those nodes, which satisfy these constraints, form the search space for top-K propagators. Our proposed algorithm is tested against four datasets and the results show that the proposed algorithm is capable of reducing the number of nodes in search space at least by 70%. We also considered the parameter ( $$\alpha$$ α and $$\beta$$ β ) dependency of Katz centrality values in our experiments and established a relationship between the $$\alpha$$ α values, number of nodes in search space and network characteristics. Later, we compare the top-K results of our approach against the top-K results of degree centrality

A Comparison of Glomerular Filtration Rate by Creatinine Based Equations and DTPA-Renogram in Healthy Adult Kidney Donors

Introduction: Accurate determination of donor kidney function has important long-term implications for both donor health and recipient outcome. Many centers use 24 hour urinary creatinine clearance or creatinine-based GFR estimations to assess kidney function but their performance when compared with GFR measurements by isotope clearance remains inconclusive. We assessed the performance of creatinine based equations against DTPA GFR for evaluating Nepalese kidney donors. Methods: All kidney donors who had undergone both DTPA GFR estimation and 24 hour urine CrCl were included. The performance of the urine-CrCl, CG-CrCl, modified MDRD GFR against DTPA GFR was evaluated by analyzing global bias, precision (R2),Pearson correlation and accuracy percentage within 30% and 15%. The sensitivity and specificity of each predictive equation in selecting donor with GFR of ≥80 mL/min/1.73 m2 was also calculated. Results: Of 51 donors analysed, only 18 (35.29%) were male. The mean measured GFR was 102.752±16.71 mL/min/1.73 m2. Of all prediction equations, urine-CrCL has most precision (R2=0.207) with the highest pearson correlation (0.455) and highest accuracy percentage within 30% and 15%. However, predictive performance was poor for all the equations. The urine CrCl had highest sensitivity of 100% for detecting donor with measured GFR>80 mL/min/1.73 m2 with positive predictive value of 92.1%. Conclusions: The performance of all equations was disappointing and even the best performing equation urine-CrCl was suboptimal for donor selection. So considering the potential risk of living kidney donation, other more accurate methods of GFR estimation should be used. _________________________________________________________ Keywords: Cockcroft-Gault equation; creatinine clearance; glomerular filtration rate; modification of diet in enal disease formula; 99mTc-Diethylene-Triamine Pentaacetic Acid

A Comparison of Glomerular Filtration Rate by Creatinine Based Equations and DTPA-Renogram in Healthy Adult Kidney Donors

Introduction: Accurate determination of donor kidney function has important long-term implications for both donor health and recipient outcome. Many centers use 24 hour urinary creatinine clearance or creatinine-based GFR estimations to assess kidney function but their performance when compared with GFR measurements by isotope clearance remains inconclusive. We assessed the performance of creatinine based equations against DTPA GFR for evaluating Nepalese kidney donors. Methods: All kidney donors who had undergone both DTPA GFR estimation and 24 hour urine CrCl were included. The performance of the urine-CrCl, CG-CrCl, modified MDRD GFR against DTPA GFR was evaluated by analyzing global bias, precision (R2),Pearson correlation and accuracy percentage within 30% and 15%. The sensitivity and specificity of each predictive equation in selecting donor with GFR of ≥80 mL/min/1.73 m2 was also calculated. Results: Of 51 donors analysed, only 18 (35.29%) were male. The mean measured GFR was 102.752±16.71 mL/min/1.73 m2. Of all prediction equations, urine-CrCL has most precision (R2=0.207) with the highest pearson correlation (0.455) and highest accuracy percentage within 30% and 15%. However, predictive performance was poor for all the equations. The urine CrCl had highest sensitivity of 100% for detecting donor with measured GFR>80 mL/min/1.73 m2 with positive predictive value of 92.1%. Conclusions: The performance of all equations was disappointing and even the best performing equation urine-CrCl was suboptimal for donor selection. So considering the potential risk of living kidney donation, other more accurate methods of GFR estimation should be used. _________________________________________________________ Keywords: Cockcroft-Gault equation; creatinine clearance; glomerular filtration rate; modification of diet in enal disease formula; 99mTc-Diethylene-Triamine Pentaacetic Acid

Central processing of leg proprioception in Drosophila

Proprioception, the sense of self-movement and position, is mediated by mechanosensory neurons that detect diverse features of body kinematics. Although proprioceptive feedback is crucial for accurate motor control, little is known about how downstream circuits transform limb sensory information to guide motor output. Here we investigate neural circuits in Drosophila that process proprioceptive information from the fly leg. We identify three cell types from distinct developmental lineages that are positioned to receive input from proprioceptor subtypes encoding tibia position, movement, and vibration. 13Bα neurons encode femur-tibia joint angle and mediate postural changes in tibia position. 9Aα neurons also drive changes in leg posture, but encode a combination of directional movement, high frequency vibration, and joint angle. Activating 10Bα neurons, which encode tibia vibration at specific joint angles, elicits pausing in walking flies. Altogether, our results reveal that central circuits integrate information across proprioceptor subtypes to construct complex sensorimotor representations that mediate diverse behaviors, including reflexive control of limb posture and detection of leg vibration.</p