An Enhanced Multi-Objective Biogeography-Based Optimization Algorithm for Automatic Detection of Overlapping Communities in a Social Network with Node Attributes

Community detection is one of the most important and interesting issues in social network analysis. In recent years, simultaneous considering of nodes' attributes and topological structures of social networks in the process of community detection has attracted the attentions of many scholars, and this consideration has been recently used in some community detection methods to increase their efficiencies and to enhance their performances in finding meaningful and relevant communities. But the problem is that most of these methods tend to find non-overlapping communities, while many real-world networks include communities that often overlap to some extent. In order to solve this problem, an evolutionary algorithm called MOBBO-OCD, which is based on multi-objective biogeography-based optimization (BBO), is proposed in this paper to automatically find overlapping communities in a social network with node attributes with synchronously considering the density of connections and the similarity of nodes' attributes in the network. In MOBBO-OCD, an extended locus-based adjacency representation called OLAR is introduced to encode and decode overlapping communities. Based on OLAR, a rank-based migration operator along with a novel two-phase mutation strategy and a new double-point crossover are used in the evolution process of MOBBO-OCD to effectively lead the population into the evolution path. In order to assess the performance of MOBBO-OCD, a new metric called alpha_SAEM is proposed in this paper, which is able to evaluate the goodness of both overlapping and non-overlapping partitions with considering the two aspects of node attributes and linkage structure. Quantitative evaluations reveal that MOBBO-OCD achieves favorable results which are quite superior to the results of 15 relevant community detection algorithms in the literature

Multi-objective community detection applied to social and COVID-19 constructed networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks. After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics. For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network. Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%

Community Detection in Networks using Bio-inspired Optimization: Latest Developments, New Results and Perspectives with a Selection of Recent Meta-Heuristics

Detecting groups within a set of interconnected nodes is a widely addressed prob- lem that can model a diversity of applications. Unfortunately, detecting the opti- mal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to pro- viding an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti-Fortunato-Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform com- petitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come

Uncovering the Structures In Ecological Networks: Multiple Techniques For Multiple Purposes

Ecosystem structure and function are the product of biological and ecological elements and their connections and interactions. Understanding structure and process in ecosystems is critical to ecological studies. Ecological networks, based on simple concepts in which biological and ecological elements are depicted as nodes with relationships between them described as links, have been recognized as a valuable means of clarifying the relationship between structures and process in ecosystems. Ecological network analysis has benefited from the advancement of techniques in social science, computer science, and mathematics, but attention must be paid to whether the designs of these techniques follow ecological principles and produce results that are ecologically meaningful and interpretable. The objective of this dissertation is to examine the suitability of these methods for various applications addressing different ecological concerns. Specifically, the studies that comprise this dissertation test methods that reveal the structure of various ecological networks by decomposing networks of interest into groups of nodes or aggregating nodes into groups. The key findings in each specific application are summarized below. In the first paper, REgionalization with Clustering And Partitioning (GraphRECAP) (Guo 2009) and Girvan and Newman\u27s method (Girvan and Newman 2002) were compared in the study of finding compartments in the habitat network of ring-tailed lemurs (Lemur catta). The compartments are groups of nodes in which lemur movements are more prevalent among the groups than across the groups. GraphRECAP found compartments with a larger minimum number of habitat patches in compartments. These compartments are considered to be more robust to local extinctions because they had stronger within-compartment dispersal, greater traversability, and more alternative routes for escape from disturbance. The potential defect of the Girvan and Newman\u27s method, an unbalanced partitioning of graphs under certain circumstances, was believed to account for its lower performance. In the second study, Modularity based Hierarchical Region Discovery (MHRD) and Edge ratio-based Hierarchical Region Discovery (EHRD) were used to detect movement patterns in trajectories of 34 cattle (Bos taurus), 30 mule deer (Odocoileus hemionus), and 38 elk (Cervus elaphus) tracked by an Automated Telemetry at Starkey National Forest, in northeastern Oregon, USA. Both methods treated animal trajectories as a spatial and ecological graph, regionalized the graph such that animals have more movement within the regions than across the regions, and then investigated the movement patterns on the basis of regions. EHRD identified regions that more effectively captured the characteristics of different species movement than MHRD. Clusters of trajectories identified by EHRD had higher cohesion within clusters and better separation between clusters on the basis of attributes of trajectories extracted from the regions. The regions detected by EHRD also served as more effective predictors for classifying trajectories of different species, achieving a higher classification accuracy with more simplicity. EHRD had better performance, because it did not rely on the null model that MHRD compared to, but invalid in this application. In the third study, a proposed Extended Additive Jaccard Similarity index (EAJS) overcame the weakness of the Additive Jaccard Similarity index (AJS) (Yodzis and Winemiller 1999) in the aggregation of species for the mammalian food web in the Serengeti ecosystem. As compared to AJS, the use of the EAJS captured the similarity between species that have equivalent trophic roles. Clusters grouped using EAJS showed higher trophic similarities between species within clusters and stronger separation between species across clusters as compared to AJS. The EAJS clusters also exhibited patterns related to habitat structure of plants and network topology associated with animal weights. The consideration of species feeding relations at a broader scale (i.e., not limited in adjacent trophic levels) accounted for the advantages of EAJS over AJS. The concluding chapter summarizes how the methods examined in the previous chapters perform in different ecological applications and examines the designs of these algorithms and whether the designs make ecological sense. It then provides valuable suggestions on the selections of methods to answer different ecological questions in practice and on the development and improvement of more ecological-oriented techniques

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Preparing for Future Forest Fires: Emerging Technologies and Innovations

Forest fires are part of the global ecosystems occurring for a long time in earth history.  These forest fires are part of the processes which establish the ecosystems and directly influence plant species composition within the ecosystems. However, the anthropogenic effect has changed this relationship causing an increasing number of forest fires Human activities have also changed world climate and future climate is expected to increase in temperature with dire consequences on the earth environment. These changes will profoundly impact on the earth’s socio-economic and human well-being. One of the effects of higher global temperature is increasing forest fires occurrences with stronger intensities.  There is a need to develop innovation and new technologies to manage these future fires. This paper aims to review various innovations and new technologies that can be used for the whole spectrum of forest fire management, from forest fire prediction to forest restoration of burnt areas. Emerging technologies such as geospatial technologies, the Internet of Things (IoT), Artificial Intelligence, 5G & enhanced connectivity, the Internet of Behaviors (IoB), virtual and augmented reality, and robotics are discussed and potential applications to forest fire management are discussed. Adaptation of these technologies is vital in the effective management of future forest fires. Key words: Climate Change, Future Fires, InnovationsKebakaran hutan merupakan bagian dari ekosistem global yang terjadi sejak lama dalam sejarah bumi. Kebakaran hutan ini merupakan bagian dari proses yang membentuk ekosistem dan secara langsung mempengaruhi komposisi spesies tumbuhan di dalam ekosistem. Namun, efek antropogenik telah mengubah hubungan ini yang menyebabkan peningkatan jumlah kebakaran hutan Aktivitas manusia juga telah mengubah iklim dunia dan iklim di masa depan diperkirakan akan meningkatkan suhu dengan konsekuensi yang mengerikan pada lingkungan bumi. Perubahan ini akan sangat berdampak pada sosial ekonomi bumi dan kesejahteraan manusia. Salah satu dampak dari peningkatan suhu global adalah meningkatnya kejadian kebakaran hutan dengan intensitas yang lebih kuat. Ada kebutuhan untuk mengembangkan inovasi dan teknologi baru untuk mengelola kebakaran di masa depan ini. Tulisan ini bertujuan untuk mengkaji berbagai inovasi dan teknologi baru yang dapat digunakan untuk seluruh spektrum penanggulangan kebakaran hutan, mulai dari prediksi kebakaran hutan hingga restorasi hutan pada kawasan yang terbakar. Teknologi yang muncul seperti teknologi geospasial, Internet of Things (IoT), Artificial Intelligence, 5G & konektivitas yang ditingkatkan, Internet of Behaviors (IoB), virtual dan augmented reality, dan robotika dibahas dan aplikasi potensial untuk manajemen kebakaran hutan dibahas. Adaptasi teknologi ini sangat penting dalam pengelolaan kebakaran hutan yang efektif di masa depan. Kata kunci: Perubahan Iklim, Kebakaran di Masa Depan, Inovas

Analysis and design of multifunctional agricultural landscapes : a graph theoretic approach

This thesis deals with the development of quantitative methodologies for the evaluation of landscape functions and their interactions in multifunctional agricultural landscapes. It focuses on the spatial coherence of hedgerow networks for ecological functions and landscape character for perception of landscape identity, and on their integration in a multifunctional and multiscale trade-off analysis. Graph theory provided the basis for new methodologies that are applied in this research

SAMPLING AND CHARACTERIZING EVOLVING COMMUNITIES IN SOCIAL NETWORKS

One of the most important structures in social networks is communities. Understanding communities is useful in many applications, such as suggesting a friend for a user in an online friendship network, recommending a product for a user in an e-commerce network, etc. However, before studying anything about communities, researchers first need to collect appropriate data. Getting complete access to the data for community studies is unrealistic in most cases. In this work, we address the problem of crawling networks to identify community structure. Firstly, we present a network sampling technique to crawl the community structure of dynamic networks when there is a limitation on the number of nodes that can be queried. The process begins by obtaining a sample for the first-time step. In subsequent time steps, the crawling process is guided by community structure discoveries made in the past. Experiments conducted on the proposed approach and certain baseline techniques reveal the proposed approach has at least a 35% performance increase in cases when the total query budget is fixed over the entire period and at least an 8% increase in cases when the query budget is fixed per time step. Secondly, we propose a sampling technique to sample communities in node attributed edge streams when there is a limit on the maximum number of nodes that can be stored. The process learns if the nodal information can characterize communities. The nodal information is leveraged with the structural information to generate representative communities. If the nodal information does not characterize communities, only structural information is considered in assigning nodes to communities. The proposed approach provides a performance improvement of up to about 5 times that of baselines. Finally, we investigate factors that characterize the evolution of communities with respect to the number of active users. We perform this investigation on the Reddit social media platform. We begin by first analyzing individual conversations of one community and sees how that generalizes to other communities. The first community studied is Reddit’s changemyview. The changemyview community, in addition to its rich data source, has an interesting property where members whose view are changed award points to users that successfully changed their minds. From the changemyview community, we observe that the linguistic style and interactions of members of the community can significantly differentiate susceptible and non-susceptible users. Next, we examine other communities (subreddits), and investigate how the user behaviors observed from changemyview relate to patterns of community evolution. We learn that the linguistic style and interactions of members in a community can also significantly differentiate the different parts of the evolution of the community with respect to number of active users