Towards a hybrid recommendation approach using a community detection and evaluation algorithm

In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy

A New Algorithm to Detect and Evaluate Learning Communities in Social Networks: Facebook Groups

This article aims to present a new method of evaluating learners by communities on Facebook groups which based on their interactions. The objective of our study is to set up a community learning structure according to the learners' levels. In this context, we have proposed a new algorithm to detect and evaluate learning communities. Our algorithm consists of two phases. The first phase aims to evaluate learners by measuring their degrees of ‘Safely’. The second phase is used to detect communities. These two phases will be repeated until the best community structure is found. Finally, we test the performance of our proposed approach on five Facebook groups. Our algorithm gives good results compared to other community detection algorithms

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak detection to maintain integrity and functionality. This study addresses gaps in traditional WDS analysis by integrating hydraulic measures with graph theory to improve sensitivity analysis for leak detection. Through case studies of five distinct WDSs, we investigate the relationship between hydraulic measures and graph theory metrics. Our findings demonstrate the collective impact of these factors on leak detection and system efficiency. The research provides enhanced insights into WDS operational dynamics and highlights the significant potential of graph theory to bolster network resilience and reliability