Community Detection in Complex Networks

Finding communities of connected individuals in social networks is essential for understanding our society and interactions within the network. Recently attention has turned to analyse these communities in complex network systems. In this thesis, we study three challenges. Firstly, analysing and evaluating the robustness of new and existing score functions as these functions are used to assess the community structure for a given network. Secondly, unfolding community structures in static social networks. Finally, detecting the dynamics of communities that change over time. The score functions are evaluated on different community structures. The behaviour of these functions is studied by migrating nodes randomly from their community to a random community in a given true partition until all nodes will be migrated far from their communities. Then Multi-Objective Evolutionary Algorithm Based Community Detection in Social Networks (MOEA-CD) is used to capture the intuition of community identi cation with dense connections within the community and sparse with others. This algorithm redirects the design of objective functions according to the nodes' relations within community and with other communities. This new model includes two new contradictory objectives, the rst is to maximise the internal neighbours for each node within a community and the second is to minimise the maximum external links for each node within a community with respect to its internal neighbours. Both of these objectives are optimised simultaneously to nd a set of estimated Pareto-optimal solutions where each solution corresponds to a network partition. Moreover, we propose a new local heuristic search, namely, the Neighbour Node Centrality (NNC) strategy which is combined with the proposed model to improve the performance of MOEA-CD to nd a local optimal solution. We also design an algorithm which produces community structures that evolve over time. Recognising that there may be many possible community structures that ex- plain the observed social network at each time step, in contrast to existing methods, which generally treat this as a coupled optimisation problem, we formulate the prob- lem in a Hidden Markov Model framework, which allows the most likely sequence of communities to be found using the Viterbi algorithm where there are many candi- date community structures which are generated using Multi-Objective Evolutionary Algorithm. To demonstrate that our study is effective, it is evaluated on synthetic and real-life dynamic networks and it is used to discover the changing Twitter communities of MPs preceding the Brexit referendum

Community Detection in Complex Networks

Finding communities of connected individuals in social networks is essential for understanding our society and interactions within the network. Recently attention has turned to analyse these communities in complex network systems. In this thesis, we study three challenges. Firstly, analysing and evaluating the robustness of new and existing score functions as these functions are used to assess the community structure for a given network. Secondly, unfolding community structures in static social networks. Finally, detecting the dynamics of communities that change over time. The score functions are evaluated on different community structures. The behaviour of these functions is studied by migrating nodes randomly from their community to a random community in a given true partition until all nodes will be migrated far from their communities. Then Multi-Objective Evolutionary Algorithm Based Community Detection in Social Networks (MOEA-CD) is used to capture the intuition of community identi cation with dense connections within the community and sparse with others. This algorithm redirects the design of objective functions according to the nodes' relations within community and with other communities. This new model includes two new contradictory objectives, the rst is to maximise the internal neighbours for each node within a community and the second is to minimise the maximum external links for each node within a community with respect to its internal neighbours. Both of these objectives are optimised simultaneously to nd a set of estimated Pareto-optimal solutions where each solution corresponds to a network partition. Moreover, we propose a new local heuristic search, namely, the Neighbour Node Centrality (NNC) strategy which is combined with the proposed model to improve the performance of MOEA-CD to nd a local optimal solution. We also design an algorithm which produces community structures that evolve over time. Recognising that there may be many possible community structures that ex- plain the observed social network at each time step, in contrast to existing methods, which generally treat this as a coupled optimisation problem, we formulate the prob- lem in a Hidden Markov Model framework, which allows the most likely sequence of communities to be found using the Viterbi algorithm where there are many candi- date community structures which are generated using Multi-Objective Evolutionary Algorithm. To demonstrate that our study is effective, it is evaluated on synthetic and real-life dynamic networks and it is used to discover the changing Twitter communities of MPs preceding the Brexit referendum

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

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

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202