A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

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

Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament

In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the VoteWatch Europe Website for the whole 7 th term (2009-2014), by considering voting similarities between Members of the EP to define weighted signed links. We then apply a selection of community detection algorithms, designed to process only positive links, to these data. We also apply Parallel Iterative Local Search (Parallel ILS), an algorithm recently proposed to identify balanced partitions in signed networks. Our results show that, contrary to the conclusions of a previous study focusing on other data, the partitions detected by ignoring or considering the negative links are indeed remarkably different for these networks. The relevance of negative links for graph partitioning therefore is an open question which should be further explored.Comment: in 2nd European Network Intelligence Conference (ENIC), Sep 2015, Karlskrona, Swede

On combinatorial optimisation in analysis of protein-protein interaction and protein folding networks

Abstract: Protein-protein interaction networks and protein folding networks represent prominent research topics at the intersection of bioinformatics and network science. In this paper, we present a study of these networks from combinatorial optimisation point of view. Using a combination of classical heuristics and stochastic optimisation techniques, we were able to identify several interesting combinatorial properties of biological networks of the COSIN project. We obtained optimal or near-optimal solutions to maximum clique and chromatic number problems for these networks. We also explore patterns of both non-overlapping and overlapping cliques in these networks. Optimal or near-optimal solutions to partitioning of these networks into non-overlapping cliques and to maximum independent set problem were discovered. Maximal cliques are explored by enumerative techniques. Domination in these networks is briefly studied, too. Applications and extensions of our findings are discussed

Distributed Multi-Objective Evolutionary Algorithm For Dynamic Multi-Characteristic Social Networks Clustering

In this information era, social media and online social networks have become a huge data source. The social network perspective provides a clear way of analyzing the structure of whole social entities. These social media and online social networks are a virtual representation of real life as they represent real life relations between social actors (people). The primary focus of this study is to propose an algorithm and its implementation for clustering of multi-characteristic dynamic graphs in general, and multi-characteristic dynamic online social networks in specific. Social networks are typically stored as graph data (edges lists mostly), and dynamically changes with time either by expanding or shrinking. The topology of the graph data also changes along with the values for the relationships between nodes. Several algorithms were proposed for clustering, but only few of them deals with multi-characteristic and dynamic networks. Most of the proposed algorithms work for static networks or small networks and a very small number of algorithms work for huge and dynamic networks. In this study a practical algorithm is proposed which uses a combination of multi-objective evolutionary algorithms, distributed file systems and nested hybrid-indexing techniques to cluster the multi-characteristic dynamic huge social networks. The results of this work show a fast clustering system that is adaptive to dynamic interactions in social networks also provides a reliable distributed framework for BIG data analysi