Towards Using Graph Analytics for Tracking Covid-19.

Development of novel combustion risk index for flammable liquids based on unsupervised clustering algorithms

Today's networks are dominantly based on the Shortest Path First (SPF) routing algorithms. These algorithms assign weighting factors to links statically, such that the routing tables are recalculated only when a topology change occurs. As a result, the network traffic is often unevenly distributed, thus causing the congestion on some links even when the total traffic load is not particularly high. In this paper, we discuss how the above-mentioned problem could be alleviated by applying dynamic routing in Software-Defined Networks (SDNs). We propose three new dynamic routing algorithms and present their OpenFlow-based implementation. The efficiency of the proposed solutions has been verified on two OpenFlow network topologies in Mininet emulator. The obtained results show that the proposed solutions could significantly improve the network throughput in comparison to SPF routing model used in traditional networks.

The key concerns to enhance the lifetime of IoT-enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are energy-efficiency and reliable data delivery under constrained resource. Traditional transmission approaches increase the communication overhead, which results in congestion and affect the reliable data delivery. Currently, many routing protocols have been proposed for UWSNs to ensure reliable data delivery and to conserve the node’s battery with minimum communication overhead (by avoiding void holes in the network). In this paper, adaptive energy-efficient routing protocols are proposed to tackle the aforementioned problems using the Shortest Path First (SPF) with least number of active nodes strategy. These novel protocols have been developed by integrating the prominent features of Forward Layered Multi-path Power Control One (FLMPC-One) routing protocol, which uses 2-hop neighbor information, Forward Layered Multi-path Power Control Two (FLMPC-Two) routing protocol, which uses 3-hop neighbor information and ’Dijkstra’ algorithm (for shortest path selection). Different Packet Sizes (PSs) with different Data Rates (DRs) are also taken into consideration to check the dynamicity of the proposed protocols. The achieved outcomes clearly validate the proposed protocols, namely: Shortest Path First using 3-hop neighbors information (SPF-Three) and Breadth First Search with Shortest Path First using 3-hop neighbors information (BFS-SPF-Three). Simulation results show the effectiveness of the proposed protocols in terms of minimum Energy Consumption (EC) and Required Packet Error Rate (RPER) with a minimum number of active nodes at the cost of affordable delay.

Mental health issues are one of the major problems in medical industries and it require an effective investigation for the recovery. Machine learning plays an important role which can be applied in mental health problems. The proposed research work describes about the ECG processing using some framework for mental health diagnostic systems. The proposed work helps to detect the values of significant ECG parameters with informative values as effective and high accuracy data predicting using the parameters as time, frequency and energy from signal of ECG. This research paper implemented with spectral clustering and radial basis function network algorithm and performed well by executing with higher accuracy of value in prediction of mental health data about the patient using machine learning approaches and techniques.

Spectral clustering is a well-known method for clustering the vertices of an undirected network. Although its use in network psychometrics has been limited, spectral clustering has a close relationship to the commonly used walktrap algorithm. In this article, we report results from simulation experiments designed to evaluate the ability of spectral clustering and the walktrap algorithm to recover underlying cluster (or community) structure in networks. The salient findings include: (a) the recovery performance of the walktrap algorithm can be improved by using K-means clustering instead of hierarchical clustering; (b) K-means and K-median clustering led to comparable recovery performance when used to cluster vertices based on the eigenvectors of Laplacian matrices in spectral clustering; (c) spectral clustering using the unnormalized Laplacian matrix generally yielded inferior cluster recovery in comparison to the other methods; (d) when the correct number of clusters was provided for the methods, spectral clustering using the normalized Laplacian matrix led to better recovery than the walktrap algorithm; and (e) when the correct number of clusters was not provided, the walktrap algorithm using the Qw modularity index was better than spectral clustering using the eigengap heuristic at determining the appropriate number of clusters. Overall, both the walktrap algorithm and spectral clustering of the normalized Laplacian matrix are effective for partitioning the vertices of undirected networks, with the latter performing better in most instances. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Recent research in machine learning has led to significant progress in various research fields. Especially, the knowledge discovery using this method in Traditional Chinese Medicine (TCM) has been becoming a hot topic. In this paper, we studied on the herb compatibility rule of insomnia using some machine learning approaches. We have extracted insomnia data set with 807 samples from the real-world Electronic Medical Records (EMRs). After cleaning and selecting the theme data referring to the prescriptions and their herbs, we constructed the herb network analysis model using the theory of complex network. In order to explore the hidden relationships among the herbs, we trained each herb node in network to obtain the herb embeddings using the Skip-Gram model in word embedding theory. After acquiring the vocabulary of herbs with the formation of vectors, we calculated the similarity among any two herb embeddings, and clustered these herb embeddings into seven communities using the Spectral Clustering (SC) algorithm. The experimental results shed light on that the methodologies used in this paper can objectively and effectively discover the relationships among herbs, and reveal the herb compatibility and herb clusters for clinical treatment research of insomnia.

Large graph analysis has become a widely studied area in recent years. Clustering is one of the most important types of analysis that has versatile applications such as community detection in social networks, image segmentation, graph partitioning, etc. However, existing clustering algorithms do not intend for large scale graphs. To solve this problem, we implemented spectral clustering in X10, that is a programming language aimed for developing highly scalable applications on Post- Petascale supercomputers. Our spectral clustering is based on the algorithm proposed by Shi and Malik. After evaluating scalability and precision, we found that our implementations are scalable in terms of execution time and precise for analyzing real data.

Social media analysis aims to collect and analyze social media user information and communication content. When people communicate through messages, phone calls, emails, and social media platforms, they leave various records on their devices and the Internet, forming a huge social network. Community detection can help investigators analyze group leaders and community structure, which is significant to further crime control, identifying coordinated campaigns, and analyzing social network dynamics. This paper proposes the application of deep learning methods for community detection. Our main idea is to utilize social network topology and social network communication content to construct user features. The proposed end-to-end community detection framework is the implementation of Graph Convolution Network and can display the social network topology, locate the core members of the community, and show the connections between users. We evaluate our framework on the Enron email dataset. Experimental results indicate that our proposed model achieves a 1.1% higher modularity score than the unsupervised benchmark methods. We also concluded that the community detection framework should be able to analyze social networks, enabling investigators to reveal connections between people.

This document describes how conventional hop-by-hop link-state routing protocols interact with new Traffic Engineering capabilities to create Interior Gateway Protocol (IGP) shortcuts. In particular, this document describes how Dijkstra's Shortest Path First (SPF) algorithm can be adapted so that link-state IGPs will calculate IP routes to forward traffic over tunnels that are set up by Traffic Engineering. This memo provides information for the Internet community.

BGP Link State (BGP-LS) Shortest Path First (SPF) Routing

Usage and Applicability of BGP Link State (BGP-LS) Shortest Path First (SPF) Routing in Data Centers

A lightweight deployment of TD routing based on SD-WANs

Community detection is an important research issue for understanding the structures of social networks. The existing community detection algorithms can be classified into two major classes: clustering based on modularity and spectral clustering. In this paper, we present a novel method for detecting community structures without prior information such as the number and size of communities in networks. This approach is based on a matrix called RS which enhances the cluster-properties in the data. We reveal the reason why a two-layer GCN is enough to achieve satisfied results. Our partition algorithm divides a network into different communities according to the similarity of the number of mutual neighbours among nodes, and makes use of a silhouette coefficient to determine the final unique partition. Experiments show that our community detection method can obtain accurate community structures on real-world networks, outperforming existing leading algorithms.

Community detection is an important problem in network clustering. This paper proposes a new network clustering method based on the control of cluster size. The word “cluster refers to the number of objects in a cluster. The optimization problem of the proposed method considers a constraint on the number of objects classified into a cluster. The proposed method accurately detects the community structure from the network data by adjusting the lower and upper limits of the cluster size as the parameters. Numerical experiments were conducted using two artificial and six benchmark datasets to verify the effectiveness of the proposed method. In the numerical experiments, the proposed method was compared with the k-medoids clustering, the Louvain method, and spectral clustering. The results show that the proposed method yields better results in terms of both clustering performance and community detection than the conventional methods.

Social media has become a one-stop shop for consuming news and expressing political views. Politics has become increasingly emotional, and the ensuing polarization has created echo chambers that favor narratives and stories that repeat only one point of view. In this article, we investigated the role of political activity through Twitter (now ‘X’) engagement as a predictor of destructive fires and deforestation in the Brazilian Legal Amazon (BLA). We used a machine learning approach based on sentiment analysis and satellite data. To test the consistency of the sentiment analysis, we compared the timing of messages related to fire and deforestation events with daily fire data from satellites. When comparing positive and negative comments about fires in the BLA, the results showed that the best model for predicting fire outbreaks is the decision tree regressor. We found evidence that positive comments about agriculture, industry, and the Amazon rainforest in response to speeches and statements by high-ranking Brazilian politicians tend to induce positive comments about fire outbreaks and deforestation. These comments then become good predictors of fire outbreaks with a 6-day lag. These results support the view that high-ranking politicians have enormous power to influence damaging events that can have severe impacts on communities, the environment, and the economy. Brazil has seen an unprecedented increase in deforestation and fires in the Amazon rainforest in recent years. Our findings contribute to the growing literature on the role of social media in real-world events and how machine learning approaches can be used to address this class of problems.