With its vast applications in the industry, computing influential nodes is becoming a popular research field in recent days. The Department of Computer Science and Engineering is delighted to inform you that the paper, Computing Influential Nodes Using Nearest Neighborhood Trust Value and Pagerank in Complex Networks have been published by Dr Murali Krishna Enduri, Assistant Professor, Dr Satish Anamalamudi, Associate Professor, and the PhD students; Koduru Hazarathaiah, Ms Srilatha Tokala in the Entropy Journal (Q2 Journal), with an impact factor 2.587.

Abstract

Computing influential nodes attract many researchers’ attention for spreading information in complex networks. It has vast applications such as viral marketing, social leaders, rumour control, and opinion monitoring. The information spreading ability of influential nodes is more compared with other nodes in the network. Several researchers proposed centrality measures to compute the influential nodes in the complex network, such as degree, betweenness, closeness, semi-local centralities, PageRank, etc. These centrality methods are defined based on the local and/or global information of nodes in the network. However, due to the high time complexity, centrality measures based on the global information of nodes have become unsuitable for large-scale networks. Very few centrality measures exist that are based on the attributes between nodes and the structure of the network. We propose the Nearest Neighbourhood Trust PageRank (NTPR) based on the structural attributes of neighbours and nearest neighbours of nodes. We define the measure based on the degree ratio, the similarity between nodes, the trust value of neighbours, and the nearest neighbours.

Explanation of the research

The research computes the influential nodes on the various real-world networks by using the proposed centrality method NTPR. The researchers find the maximum influence by using influential nodes with SIR and independent cascade methods. They also compare the maximum influence of our centrality measure with the existing basic centrality measures.

Social implications

Viral Marketing is a business strategy that uses existing social networks to promote products. The influential nodes in complex networks can be found using the centrality measure and can be used as the seed nodes for promoting products in the social networks. A rumour is a statement being said without knowing if it is true or not. The rumours can be easily controlled by discovering influential nodes. The researchers look forward to finding a centrality measure to detect the influential nodes efficiently.

SRM University-AP preserves a research-empowered ecosystem stimulating its faculty and students to roll out original and discerning studies capable of making instrumental contributions aiming the scientific and societal progress. Making strides with impactful research publications and groundbreaking achievements, the institution has carved a niche for itself in the academic milieu. We are glad to present yet another success story of our research community that keeps bringing laurels to the institutions from far and wide.

Dr Pradyut Kumar Sanki and his PhD scholar Bevara Vasudeva, from the Department of Electronics and Communications Engineering, along with a group of Computer Science and Engineering students: Medarametla Depthi Supriya, Devireddy Vignesh, Peram Bhanu Sai Harshath, and Sravya Kuchina have got their paper titled ‘’VLSI Implementation of a Real-Time Modified Decision-Based Algorithm for Impulse Noise Removal’’ accepted in the IEEE conference IEMTRONICS 2022. This publication is a part of the Capstone project contributed by the students.

IEMTRONICS 2022 (International IOT, Electronics and Mechatronics Conference) is an international conclave that aims to bring together scholars from different backgrounds to disseminate inventive ideas in the fields of IOT, Electronics and Mechatronics. The conference will also promote an intense dialogue between academia and industry to bridge the gap between academic research, industry initiatives, and governmental policies. This is fostered by panel discussions, invited talks, and industry exhibits where academia and industry will mutually benefit from each other.

Through the research paper, the team proposes a real-time impulse noise removal (RTINR) algorithm and its hardware architecture for denoising images corrupted with fixed valued impulse noise.

Abstract of the Research

A decision-based algorithm is modified in the proposed RTINR algorithm where the corrupted pixel is first detected and is restored with median or previous pixel value depending on the number of corrupted pixels in the image. The proposed RTINR architecture has been designed to reduce the hardware complexity as it requires 21 comparators, 4 adders, and 2 line buffers which in turn improve the execution time. The proposed architecture results better in qualitative and quantitative performance in comparison to different denoising schemes while evaluated based on the following parameters: PSNR, IEF, MSE, EKI, SSIM, FOM, and visual quality. The proposed architecture has been simulated using the XC7VX330T-FFG1761 VIRTEX7 FPGA device and the reported maximum post place and route frequency is 360.88 MHz. The proposed RTINR architecture is capable of denoising images of size 512 × 512 at 686 frames per second. The architecture has also been synthesized using UMC 90 nm technology where 103 mW power is consumed at a clock frequency of 100 MHz with a gate count of 2.3K (NAND2) including two memory buffers.