ABSTRACT We propose an incremental similarity‐based label propagation algorithm (DLPA‐S) for detecting dynamic community structures. As the network evolves, the method efficiently updates the communities over time via local label updates driven by changes in network topology—including edge and vertex additions or removals—and vertex similarity. This incremental approach significantly reduces computational cost while preserving accuracy in capturing community evolution. We evaluate DLPA‐S using a comprehensive set of quality metrics that assess both the structural properties of the network and the agreement between detected communities and ground‐truth partitions. Experiments are conducted on synthetic and real‐world dynamic networks, varying key graph characteristics such as the number of vertices and the average degree, as well as across diverse community scenarios. The results show that DLPA‐S consistently achieves stable and high‐performing results, maintains high NMI and F1 scores, ensures strong internal connectivity, clear community separability, and avoids disconnected communities, while remaining computationally efficient.

ABSTRACT Monitoring network traffic and detecting security threats is a vital task in today's world, and intrusion detection systems (IDS) have become an essential tool for this purpose. However, IDSs have to analyze large volumes of data, which often contain irrelevant and redundant features. This makes the job of IDSs more challenging, as they must sift through all available features to identify attack patterns, leading to longer processing time and reduced detection accuracy. To address this, we propose a new wrapper approach for solving the feature selection (FS) problem. Our proposed approach uses a novel multi‐objective binary bat algorithm (MBBA‐FS) with a decision tree classifier. The MBBA‐FS aims to produce a set of non‐dominated solutions that minimize the number of features used while maintaining a high detection accuracy. Then, we use a frequency ranking method to identify a single subset of relevant features from the resulting set of non‐dominated solutions. We tested the feasibility and performance of our approach against other leading FS methods using various datasets, including KDD CUP 1999, NLS‐KDD, UNSW‐NB15, and several synthetic benchmarks. The experimental results show that MBBA‐FS outperforms existing FS approaches in terms of classification accuracy and number of selected features.

Abstract Sustainable agriculture is becoming increasingly important in the face of growing environmental challenges. One key aspect of sustainable agriculture is managing soil resources effectively. In this context, digital soil mapping (DSM) has emerged as a powerful tool to understand soil variability better and inform land management decisions. This paper proposes a comprehensive data warehouse for DSM that supports climate change analysis. Our architecture integrates frequent itemset mining (FMI) and association rules mining (ARM) to extract insights from large‐scale soil data. We review related studies in soil data warehousing and ARM, identify gaps, and propose a data warehouse architecture leveraging the galaxy multidimensional model for DSM based on the SCORPAN model, which incorporates all relevant soil forming factors. We employ and compare A‐priori, FP‐growth, and ECLAT algorithms to efficiently mine frequent itemsets and generate association rules. Our intensive experiments evaluation demonstrates that FP‐growth outperforms the other algorithms in accuracy, scalability, and speed and requires less memory. Additionally, we utilized correlation metrics for ARM, such as lift, cosine, kulc, and Imbalance ratio, to obtain the most significant and relevant association rules. These rules provide valuable insights into the complex relationships between soil properties and environmental factors, which can inform land management decisions and improve sustainable agriculture practices. This work contributes to the growing body of research on DSM and data‐driven approaches to sustainable agriculture.

Summary Detecting hiding communities is considered as a main topic in complex networks. In this article, we propose a multi‐swarm elephant herding optimization (EHO) algorithm to uncover community structures in complex environments. It adapts EHO algorithm to community detection problem. EHO algorithm relies on two procedures which are updating clan procedure and separating procedure. The main idea of our multi‐swarm approach is that the population is composed of a set of interacting clans. In each clan, a local search function is defined to determine best local individual called matriarch. Through updating clan procedure, the remaining individuals in the clan update their positions based on the matriarch position. In addition, to ensure significant individuals in the clan, a multi‐swarm cooperative algorithm is designed to implement separating procedure; clans interchange individuals to balance the exploration and exploitation abilities. A series of experiments are carried out on artificial and real networks. The results obtained by the proposed approach are better than the results obtained by some other approaches.

Clustering is partitioning data into groups, such that data in the same group are similar. Many clustering algorithms are proposed in the literature. K-means is the most used one because of its implementation simplicity and efficiency. Many clustering algorithms are based on the K-means algorithms aiming to improve execution time or clustering quality or both of them. Improving clustering quality can be done by an optimal selection of the initial centroids using for example meta-heuristics. Improving execution time can be performed using parallelism. In this paper, we propose a parallel hybrid K-means based on Google's MapReduce framework for the parallelism and the PSO meta-heuristics for the choice of the initial centroids. This algorithm is used to cluster multi-dimensional data sets. The results proved that using a network of machines to process data improves the execution time and the clustering quality.