Algorithm For Community Detection In Networks Research Articles

A Novel Water Supply Network Sectorization Methodology Based on a Complete Economic Analysis, Including Uncertainties

The core idea behind sectorization of Water Supply Networks (WSNs) is to establish areas partially isolated from the rest of the network to improve operational control. Besides the benefits associated with sectorization, some drawbacks must be taken into consideration by water operators: the economic investment associated with both boundary valves and flowmeters and the reduction of both pressure and system resilience. The target of sectorization is to properly balance these negative and positive aspects. Sectorization methodologies addressing the economic aspects mainly consider costs of valves and flowmeters and of energy, and the benefits in terms of water saving linked to pressure reduction. However, sectorization entails other benefits, such as the reduction of domestic consumption, the reduction of burst frequency and the enhanced capacity to detect and intervene over future leakage events. We implement a development proposed by the International Water Association (IWA) to estimate the aforementioned benefits. Such a development is integrated in a novel sectorization methodology based on a social network community detection algorithm, combined with a genetic algorithm optimization method and Monte Carlo simulation. The methodology is implemented over a fraction of the WSN of Managua city, capital of Nicaragua, generating a net benefit of 25,572 $/year.

NCSS: an effective and efficient complex network community detection algorithm

NCSS: an effective and efficient complex network community detection algorithm

Parallel overlapping community detection algorithm in complex networks based on label propagation

Some of the defects of community detection algorithms in complex networks include pre-parameter limits and redundant labels. Accordingly, we present an improved overlapping community detection algorithm in complex networks that is a minimal maximal clique label propagation algorithm (MMCLPA). The algorithm uses the minimal maximal clique (MMC), and allows each MMC to share the same label by reducing redundant labels and random factors. Therefore, the MMCLPA algorithm increases the stability of the algorithm. Mean-while, the MMCLPA algorithm completes label propagation from core node groups (MMC) for distribution using intimacy as the weight of the label. For the labels post processing, uses adaptive threshold method to overcome pre-parameter limitations in unknown complex networks. When compared with other community detection al-gorithms using arti cial and real network data, our experiment results prove that the MMCLPA algorithm not only increases the tolerance for mixing parameters, but also improves the robustness of the algorithm. Lastly, by employing a distributed computing model (Map Reduce) and cloud platform (Hadoop), we allow parallelization implementation of MMCLPA. Our experiment results show that parallel MMCLPA owns an approximation of quality compared with the stand-alone system, as well as better scalability.

基于标签传播的可并行复杂网络重叠社区发现算法

Some of the defects of community detection algorithms in complex networks include pre-parameter limits and redundant labels. Accordingly, we present an improved overlapping community detection algorithm in complex networks that is a minimal maximal clique label propagation algorithm (MMCLPA). The algorithm uses the minimal maximal clique (MMC), and allows each MMC to share the same label by reducing redundant labels and random factors. Therefore, the MMCLPA algorithm increases the stability of the algorithm. Mean-while, the MMCLPA algorithm completes label propagation from core node groups (MMC) for distribution using intimacy as the weight of the label. For the labels post processing, uses adaptive threshold method to overcome pre-parameter limitations in unknown complex networks. When compared with other community detection al-gorithms using arti cial and real network data, our experiment results prove that the MMCLPA algorithm not only increases the tolerance for mixing parameters, but also improves the robustness of the algorithm. Lastly, by employing a distributed computing model (Map Reduce) and cloud platform (Hadoop), we allow parallelization implementation of MMCLPA. Our experiment results show that parallel MMCLPA owns an approximation of quality compared with the stand-alone system, as well as better scalability.

New Methods for Generating Synthetic Equivalents of Real Social Networks

In both empirical and theoretical contexts, we may need to generate synthetic versions of large-scale social networks. Extant network generation models and algorithms do not replicate in full measure the structural complexities of real social networks. While extant approaches can replicate specific network characteristics well (e.g., degree distribution), they fail to replicate other aspects such as presence of overlapping cliques and community structures in social networks. Inspired by some recent developments in community detection algorithms in social networks, we propose two new network generation algorithms that explicitly incorporate the distribution of overlapping communities. We show that our proposed algorithms are measurably superior to the extant algorithms in generating synthetic social networks that more closely resemble actual social networks along a multiplicity of metrics that are used to characterize such networks.

An Analysis of Overlapping Community Detection Algorithms in Social Networks

In the field of research, Social Network Analysis is prevalent domain which pulls the attention of many data mining experts. Social network analysis is the specific field of sociology and anthropology. It shares a number of characteristics common to real network. Some real networks like Facebook, Twitter exhibit the concept of community structure within the network. Social network is represented as a network graph. Detecting the communities involves finding the densely connected nodes. Overlapping communities are possible if a node is a member of more than one community. This paper discusses various modularity based approaches on detecting the overlapping communities in the social networks. This work aims in providing the characteristics and limitations of modularity based overlapping community detection algorithms.

Musical rhythmic pattern extraction using relevance of communities in networks

The rhythmic background of a musical piece is usually composed of featured elements that define the musical genre. For each song, such elements form rhythmic patterns, the most repetitive ones defines the rhythm of a specific musical genre and the less frequent ones correspond to fortuitous patterns that comply a transition, diversification, introduction or conclusion function. In this paper, we introduce a network-based method for automatic extraction of rhythmic pattern of a single song. We also propose a method for rhythmic summarization of a set of songs from the same genre, artist, and combinations of genres and artists. The method can be used to extract any type of rhythm pattern, both monophonic and polyphonic, represented by symbolic data. A musical piece is generally formed by one or more predefined rhythmic patterns and such patterns are composed of rhythmic cells (RCs), which are groups of rhythmic figures derived from nth division of a larger rhythmic figure. At the pre-processing and encoding phases of the proposed method, the RCs of drums percussion lines are represented in duration-weighted notation (DWN). Then, the vector of DWN is encoded to be free of the dimensional dependence on the number of figures in the RC. After that, a network is constructed from the encoded DWN using the method proposed in this paper. We find that the rhythmic patterns of the musical work are related to the formation of communities in the constructed music network. In order to confirm such a finding, three network community detection algorithms are applied: Modularity Optimization algorithm and Louvain algorithm for the disjoint community detection, and Bayesian Non-negative Matrix Factorization algorithm for detecting overlapped communities. Furthermore, a new measure for quantifying the relevance of communities to differentiate types of rhythmic patterns is introduced. The proposed technique has been applied to automatic extraction of rhythmic pattern and rhythmic summarization of the songs of The Beatles, Bob Marley, and other artists, respectively. The results show that the method of extraction and summarization has good performance.

A novel disjoint community detection algorithm for social networks based on backbone degree and expansion

Community detection in social networks is a key point to discover the functions and structure of social networks. A great deal of work has been done for overlapping community detection and disjoint community detection, and numerous techniques such as spectral clustering, modularity maximization, random walks, differential equation, and statistical mechanics are used to identify a community in networks, but most of these work adopts pure mathematic and physical methods to discover communities from social networks, on the contrary ignoring the social and biological properties of communities and social networks. In this paper, firstly we propose the community forest model based on these social and biological properties to characterize the structure of real-world large-scale networks, secondly we mainly define a new metric named backbone degree to measure the strength of the edge and the similarity of vertices and give a new sense definition to community based on expansion, thirdly we develop a novel algorithm that based on backbone degree and expansion to discover disjoint communities from real social networks. This algorithm has better performance and effects compared with CNM and GN algorithms in computational cost and visibility. It has worked well on Email-Enron, American College Football, karate club etc. data sets.

Flow networks: a characterization of geophysical fluid transport.

We represent transport between different regions of a fluid domain by flow networks, constructed from the discrete representation of the Perron-Frobenius or transfer operator associated to the fluid advection dynamics. The procedure is useful to analyze fluid dynamics in geophysical contexts, as illustrated by the construction of a flow network associated to the surface circulation in the Mediterranean sea. We use network-theory tools to analyze the flow network and gain insights into transport processes. In particular, we quantitatively relate dispersion and mixing characteristics, classically quantified by Lyapunov exponents, to the degree of the network nodes. A family of network entropies is defined from the network adjacency matrix and related to the statistics of stretching in the fluid, in particular, to the Lyapunov exponent field. Finally, we use a network community detection algorithm, Infomap, to partition the Mediterranean network into coherent regions, i.e., areas internally well mixed, but with little fluid interchange between them.

Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA) based on kernelk-means (KKM) and ratio cut (RC) objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-of-the-art approaches.

Community Detection in Directed Weighted Function-call Networks

Complex network is being considered as a key approach to representing complex systems. We have focused on the static analyses of software systems with the function-call graph and empirically found them to reveal small-world, scale-free features similar to those identified in sociological and technological systems. Another crucial step when studying the structure of networks is to identify communities. Most existing approach to this problem in the previous literature simply ignored the edge direction and applied methods for community detecting in undirected networks. In this paper, we consider the problem of finding communities in directed weighted networks and develop a new community detection algorithm for the networks. Our method regards communities as groups of links rather than nodes in contrast to the existing detection algorithms. This algorithm is tested on function-call graph of artificial system and our experiment is shown to finding communities on the test directed weighted network successfully.

A semantic overlapping community detection algorithm based on field sampling

The traditional semantic social network (SSN) community detection algorithms need to preset the number of the communities and could not detect the overlapping communities. To solve the issue of presetting the number of communities, we present a clustering algorithm for community detection based on the link-field-topic (LFT) model suggested. For the process of clustering is independent of context sampling, the number of communities is not necessary to be preset. To solve the issue of overlapping community detection, we establish the semantic link weight (SLW) depending on the analysis of LFT, to evaluate the semantic weight of links for each sampling field. The proposed clustering algorithm is based on the SLW which could separate the SSN into clustering units. As a result, the intersection on several units is the overlapping part. Finally, we establish semantic modularity (SQ) involving SQ1 and SQ2 for the evaluation of the detected semantic communities. The efficiency and feasibility of the LFT model and the semantic modularity is verified by experimental analysis.

Multi-scale analysis of the European airspace using network community detection.

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.

Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

COMMUNITY DETECTION IN SOCIAL NETWORKS: AN OVERVIEW

A social network can be defined as a set of people connected by a set of people. Social network analysis provides both a visual and a mathematical analysis of human relationship. The investigation of the community structure in the social network has been the important issue in many domains and disciplines. Community structure assumes more significance with the increasing popularity of online social network services like Facebook, MySpace, or Twitter. This paper reflects the emergence of communities that occur in the structure of social networks, represented as graphs. We have mainly discussed various community detection algorithms in real world networks in this paper. This paper represents as an overview of the community detection algorithms in social networks.

Network community detection algorithm by integrating edge and vertices information for uncovering the interaction patterns of marine microbial species

With the development of high-throughput and low-cost sequencing technology, a large amount of marine microbial sequences are generated. So, it is possible to research more uncultivated marine microbes. The interaction patterns of marine microbial species and marine microbial diversity hidden in these large amount sequences can not be deeply mined with the conventional statistical methods and analytical software. In this paper, we defined the importance measures of network edges and vertex, identified the key vertices of the community, then defined the confidence of one vertices belonging to the community. In the end, we proposed the community detection algorithm by integrating the edge and vertices information in complex network (CDIEV), and also applied it to uncover the seasonal marine microbial interaction patterns. After the marine microbial 16S RNA sequences clustered into operational taxonomic units (OTUs) at 99% sequence similarity with ESPRIT algorithm, the four seasonal (spring, summer, autumn, winter) marine microbial interaction networks based on the spearman correlation and P values were constructed. The experimental results of CDIEV and analysis of network topological parameters show that the four seasonal marine microbial interaction networks have the characters of small-world and scale-free of complex network; the marine microbial interaction patterns have some difference among the four seasonal marine microbial interaction networks; CDIEV algorithm can effectively mine the network community, but the selection of threshold value b affects the community detection results.

Community detection in disease-gene network based on principal component analysis

The identification of communities is imperative in the understanding of network structures and functions.Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.