Ahmed Ibrahem Hafez

Genetic Algorithms for community detection in social networks

Community detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this results to a solution with a particular community structure property. More recently researchers viewed the problem as a multi-objective optimization problem and many approaches have been proposed to solve it. However which objective functions could be used with each other is still under debated since many objective functions have been proposed over the past years and in somehow most of them are similar in definition. In this paper we use Genetic Algorithm (GA) as an effective optimization technique to solve the community detection problem as a single-objective and multi-objective problem, we use the most popular objectives proposed over the past years, and we show how those objective correlate with each other, and their performances when they are used in the single-objective Genetic Algorithm and the Multi-Objective Genetic Algorithm and the community structure properties they tend to produce.

Networks Community Detection Using Artificial Bee Colony Swarm Optimization

Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this work Artificial bee colony (ABC) optimization has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures when used as an objective function within ABC. Experiments on real life networks show the capability of the ABC to successfully find an optimized community structure based on the quality function used.

Community Detection Algorithm Based on Artificial Fish Swarm Optimization

Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this paper Artificial Fish Swarm optimization (AFSO) has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures and other well-known methods. Experiments on real life networks show the capability of the AFSO to successfully find an optimized community structure based on the quality function used.

A Discrete Bat Algorithm for the Community Detection Problem

Community detection in networks has raised an important research topic in recent years. The problem of detecting communities can be modeled as an optimization problem where a quality objective function that captures the intuition of a community as a set of nodes with better internal connectivity than external connectivity is selected to be optimized. In this work the Bat algorithmwas used as an optimization algorithm to solve the community detection problem. Bat algorithm is a new Nature-inspired metaheuristic algorithm that proved its good performance in a variety of applications. However, the algorithm performance is influenced directly by the quality function used in the optimization process. Experiments on real life networks show the ability of the Bat algorithm to successfully discover an optimized community structure based on the quality function used and also demonstrate the limitations of the BA when applied to the community detection problem.

Genetic Algorithms for Multi-Objective Community Detection in Complex Networks

Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure–function relationship. Therefore, detecting communities (or modules) can be a way to identify substructures that could correspond to important functions. Community detection can be viewed as an optimization problem in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the detection problem. However, those approaches have drawbacks because they attempt to optimize only one objective function, this results in a solution with a particular community structure property. More recently, researchers have viewed the community detection problem as a multi-objective optimization problem, and many approaches have been proposed. Genetic Algorithms (GA) have been used as an effective optimization technique to solve both single- and multi-objective community detection problems. However, the most appropriate objective functions to be used with each other are still under debate since many similar objective functions have been proposed over the years. We show how those objectives correlate, investigate their performance when they are used in both the single- and multi-objective GA, and determine the community structure properties they tend to produce.

A discrete Krill herd optimization algorithm for community detection

The rapid increase on the social networks presents an urgent need for identifying the community detection. Community detection is process of defining complex networks topology or structure using an objective quality function by clustering or grouping these complex networks as sets or groups of nodes and edges based on their connectivity. This paper presents a discrete Krill herd swarm optimization algorithm for community detection problem (AKHSO) as an efficient optimization technique to handle the problem of complex networks community detection. AKHSO is able to define dynamically the number of communities in the process. A comparison is conducted with well-known community quality measures and benchmarks. The experiment is executed on real life popular benchmarks data sets. The experiment proved that AKHSO can handle the community detection problem and define the structure of complex networks with high accuracy and quality.

Hybrid Monkey Algorithm with Krill Herd Algorithm optimization for feature selection

In this work, a system for feature selection based on hybrid Monkey Algorithm (MA) with Krill Herd Algorithm (KHA) is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attribute. A system for feature selection is proposed in this work using a hybrid Monkey Algorithm and Krill Herd Algorithm (MAKHA). The MAKHA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. MAKHA is a new evolutionary computation technique, inspired by the chicken movement. The MAKHA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on 18 data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

Testing Community Detection Algorithms: A Closer Look at Datasets

Social networks of various kinds demonstrate a strong community effect. Actors in a network tend to form closely-knit groups; those groups are also called communities or clusters. Detecting such groups in a social network (i.e., community detection) remains a core problem in social network analysis. Among the challenges that face the researchers to come up with advanced community detection methods, there is a key challenge, which is the validation and evaluation of their methods. The limited benchmark data available, the lack of ground truth for many of the available network datasets, and the nature of the social behavior factor in the problem, turned the evaluation process to be very hard. Accordingly, understanding such challenges may help in designing good community detection methods. This chapter presents testing strategies for community detection approaches and explores a number of datasets that could be used in the testing process as well as stating some characteristics of those datasets.

A Multi-Objective Genetic Algorithm for Community Detection in Multidimensional Social Network

Multidimensionality in social networks is a great issue that came out into view as a result of that most social media sites such as Facebook, Twitter, and YouTube allow people to interact with each other through different social activities. The community detection in such multidimensional social networks has attracted a lot of attention in the recent years. When dealing with these networks the concept of community detection changes to be, the discovery of the shared group structure across all network dimensions such that members in the same group interact with each other more frequently than those outside the group. Most of the studies presented on the topic of community detection assume that there is only one kind of relation in the network. In this paper, we propose a multi-objective approach, named MOGA-MDNet, to discover communities in multidimensional networks, by applying genetic algorithms. The method aims to find community structure that simultaneously maximizes modularity, as an objective function, in all network dimensions. This method does not need any prior knowledge about number of communities. Experiments on synthetic and real life networks show the capability of the proposed algorithm to successfully detect the structure hidden within these networks.

Community detection in social networks by using Bayesian network and Expectation Maximization technique

Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure-function relationship; therefore, detecting communities can be a way to identify substructures that could correspond to important functions. Social networks can be formalized by a statistical model in which interactions between actors are generated based on some assumptions. We adopt the idea and introduce a statistical model of the interactions between social networks actors, and we use Bayesian network (probabilistic graphical model) to show the relation between model variables. Through the use Expectation Maximization (EM) algorithm, we drive estimates for the model parameters and propose a community detection algorithm based on the EM estimates. The proposed algorithm works well with directed and undirected networks, and with weighted and un-weighted networks. The algorithm yields very promising results when applied to the community detection problem.