Hierarchical Community Structure Research Articles

Prediction of missing links based on multi-resolution community division

The investigation of link prediction in networks is an important issue in many disciplines. The research of prediction algorithms which required short time but high accuracy is still a challenging task. Most of the existing algorithms are based on the topological information of the networks, including the local or global similarity indices. It is found that the hierarchical organization and community structure information may indeed provide insights for link prediction. In this paper, we propose a simple link prediction method, which fully explore the community structure information of the networks. Firstly, the community structure of the networks under different resolutions is extracted. Then, a simple frequency statistical model is applied to calculate how many times that a pair of nodes divided into the same community under different resolutions. Finally, the probability of the missing links is calculated. The performance of our algorithm is demonstrated by comparing with other seven well-known methods on two kinds of networks in different scales. The results indicate that our approach not only has a good performance on the accuracy, but also has a lower time complexity than any other algorithms which are based on hierarchical structure of the network.

A Community Divisive Algorithm Based on Local Weak Edges

It is indicated that community structure is a common property in complex network. Detecting community structure can give a significant insight to structural and functional properties of complex network. In this paper, we propose an divisive algorithm to detect hierarchical community structure in complex network. By regarding nodes and communities as random variables, the algorithm uses correlation coefficient to calculate similarity of nodes, and then finds and removes set of local weak edges in complex network until the reasonable hierarchical communities are revealed. Experimental results in real-world and artificial networks demonstrate that the proposed algorithm is more accurate than existing mechanisms.

Hierarchical Community Structure in Complex (Social) Networks

The investigation of community structure in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science where systems are often represented as graphs. One of the challenges is to find local communities from a local viewpoint in a graph without global information in order to reproduce the subjective hierarchical vision for each vertex. In this paper we present the improvement of an information dynamics algorithm in which the label propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints \cite{Massaro2012}. In this framework we have introduced two more complex heuristics that allow the algorithm to detect the multi-resolution hierarchical community structure of networks from a source vertex or communities adopting fixed values of model's parameters. Experimental results show that the proposed methods are efficient and well-behaved in both real-world and synthetic networks.

A Multi-Agent School Simulation Based on Hierarchical Social Networks

The quality of K-12 education has been a very big concern for years. Previous methods studied only one or two factors, such as school choice, or teacher quality, on school performance. Therefore the results they provide can be limited. We propose a multi-agent approach to integrate multiple actors in a school system. These actors include teachers, students, supporting staffs and administrators. The interactions among these actors compose a hierarchical school social network. We first detect the hierarchical community structure in this school network by using an agglomerative hierarchical algorithm. Existing agglomerative hierarchical algorithms usually calculate similarity or dissimilarity between two clusters by using some measure of distance between pairs of observations. We, however, develop a method that calculates similarity based on social interactions between interactions is essential in multi-agent systems. Our algorithm is applied to 15 school districts in Bexar County, Texas, and it provides satisfying results on generating the hierarchical structure of all school districts. We then use the detected structure of the social network to evaluate the school system’s organization performance. We design and implement a funding evaluation model to decompose the funding policy task into subtasks and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. Experiments in the 15 school districts in Bexar County show no significant correlation between student performance and the amount of the funding a school district received.

Multi-objective particle swarm optimization with decomposition for network community discovery

A multi-objective particle swarm optimization with decomposition for network community discovery was proposed and the multi-objective optimization model of community discovery was constructed through comparing the optimization objectives of different community discovery algorithms in social network. The proposed algorithm adopted the Chebyshev method to decompose the multi-objective optimization problem into a number of single-objective optimization subproblems and used Particle Swarm Optimization( PSO) to discover the community structure. Moreover, a new local search based mutation strategy was put forward to improve the search efficiency and speed up convergence. The proposed algorithm overcame the defects of single objective optimization methods. The experimental results on synthetic networks and real-world networks show that the proposed algorithm can discover the community structure rapidly and accurately and reveal the hierarchical community structure.

A new fingerprinting scheme using social network analysis for majority attack

Digital fingerprinting is a technology to protect multimedia content from unauthorized redistribution. However, collusion attack is a cost-efficient attack for digital fingerprinting, where groups of dishonest users create a pirate copy using their copies for the purpose of attenuating or removing the fingerprints. In this paper, FCBSN, Fingerprinting Code Based on Social Networks, for coding the user's fingerprints to resist majority attack, is proposed. The proposed scheme stems from the concept of coalition which always occurred in a social network. Different from all existing work, we explore the notion of the hierarchical community structure of social network and its intrinsic properties to assign fingerprints to users, drawing on the social relation according to the similar metric between two users. Theoretical analysis and experimental results show that the FCBSN detector outperforms the existing group detector for BS code and Tardos detector by large margins.

Spanning tree separation reveals community structure in networks

We present a simple, intuitive, and effective approach for network clustering. It is based on basic concepts of linear algebra such as efficient calculation of spanning trees, and can be implemented in a few lines of code. We introduce the node separation measure spanning tree separation (STS) and the corresponding graph distance measure spanning tree vector similarity distance (STVSD). We demonstrate that the STS is a link salience measure able to identify the backbone of networks. The STVSD is used to reveal the hierarchical community structure of networks. We show that it, together with the clustering quality measure partition density, is on a par with the best graph or network clustering methods known, in terms of both quality and efficiency. In perspective, we note that our approach could also handle weighted and directed networks and could be used for identification of overlapping communities.

A Micro-Community Structure Merging Model Using a Community Sample Matrix

Detecting an overlapping and hierarchical community structure can give a significant insight into structural and functional properties in complex networks. We propose a micro-community structure merging model to detect overlapping and hierarchical communities. The algorithm maps communities to random variables using the community sample matrix to evaluate similarity between communities. After finding density-based micro-community structures, the algorithm merges these reasonable micro-communities iteratively to form communities. Simulation results in three real networks show that the proposed algorithm is more accurate than some existing mechanisms. In this way, we can obtain a detailed understanding of the overlapping and hierarchical communities.

Non-negative sparse autoencoder neural networks for the detection of overlapping, hierarchical communities in networked datasets

We propose the first use of a non-negative sparse autoencoder (NNSAE) neural network for community structure detection in complex networks. The NNSAE learns a compressed representation of a set of fixed-length, weighted random walks over the network, and communities are detected as subsets of network nodes corresponding to non-negligible elements of the basis vectors of this compression. The NNSAE model is efficient and online. When utilized for community structure detection, it is able to uncover potentially overlapping and hierarchical community structure in large networks.

UNFOLD SYNCHRONIZATION COMMUNITY STRUCTURE USING MARKOV AND SPECTRAL SIGNATURE

Synchronization is a powerful basic rule in nature that regulates a large variety of complex processes, and global synchronization phenomena have been extensively studied for modeling and analyzing the dynamic of systems. Distinct from the existing studies, the work presented here explores the notion of local synchronization phenomenon and their intrinsic properties from the dynamics of a stochastic model defined on a complex network. The relationship between the hierarchical community structure of the network and the local synchronization properties of such a stochastic model has been established based on the famous Kuramoto Model. Critical topological information regarding to the community structures of the network can be inferred from their spectral signatures. Utilizing a novel dynamic system, we have developed an efficient two stages model which can efficiently uncover natural communities hidden in networks in a scalable manner. The effectiveness and efficiency of the algorithm have been theoretically analyzed as well as experimentally validated and it overcomes the inefficiency of the existing methods.

Potts model based on a Markov process computation solves the community structure problem effectively

The Potts model is a powerful tool to uncover community structure in complex networks. Here, we propose a framework to reveal the optimal number of communities and stability of network structure by quantitatively analyzing the dynamics of the Potts model. Specifically we model the community structure detection Potts procedure by a Markov process, which has a clear mathematical explanation. Then we show that the local uniform behavior of spin values across multiple timescales in the representation of the Markov variables could naturally reveal the network's hierarchical community structure. In addition, critical topological information regarding multivariate spin configuration could also be inferred from the spectral signatures of the Markov process. Finally an algorithm is developed to determine fuzzy communities based on the optimal number of communities and the stability across multiple timescales. The effectiveness and efficiency of our algorithm are theoretically analyzed as well as experimentally validated.

On the Spectral Characterization and Scalable Mining of Network Communities

Network communities refer to groups of vertices within which their connecting links are dense but between which they are sparse. A network community mining problem (or NCMP for short) is concerned with the problem of finding all such communities from a given network. A wide variety of applications can be formulated as NCMPs, ranging from social and/or biological network analysis to web mining and searching. So far, many algorithms addressing NCMPs have been developed and most of them fall into the categories of either optimization based or heuristic methods. Distinct from the existing studies, the work presented in this paper explores the notion of network communities and their properties based on the dynamics of a stochastic model naturally introduced. In the paper, a relationship between the hierarchical community structure of a network and the local mixing properties of such a stochastic model has been established with the large-deviation theory. Topological information regarding to the community structures hidden in networks can be inferred from their spectral signatures. Based on the above-mentioned relationship, this work proposes a general framework for characterizing, analyzing, and mining network communities. Utilizing the two basic properties of metastability, i.e., being locally uniform and temporarily fixed, an efficient implementation of the framework, called the LM algorithm, has been developed that can scalably mine communities hidden in large-scale networks. The effectiveness and efficiency of the LM algorithm have been theoretically analyzed as well as experimentally validated.

Mining Hierarchical Community Structure Within Networks from Density-Connected Traveling Orders

PDF HTML阅读 XML下载 导出引用 引用提醒 从链接密度遍历序列中挖掘网络社团的层次结构 DOI: 10.3724/SP.J.1001.2011.03939 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(60933009); 陕西省自然科学基础研究计划(SJ08-ZT14) Mining Hierarchical Community Structure Within Networks from Density-Connected Traveling Orders Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:提出一种称为TRAVEL 的网络聚类算法.它能够产生包含所有可能密度聚类的网络链接遍历序列,并从中自动发现网络的全局优化聚类.然后,遍历序列被转换为连续子区间堆结构.在此基础上,提出一种聚类算法HCLU,可以无须用户干预地从连续子区间堆中自动发现网络的层次聚类边界.在真实网络以及计算机生成的仿真网络数据集上的实验结果表明,所提出的算法比目前的基准方法具有更高的聚类精度.此外,算法能够从各种带有噪声的网络中发现无冗余且鲁棒的层次社团结构. Abstract:This paper proposes a density-based network clustering algorithm, TRAVEL. The algorithm produces a traveling order containing clustering with various densities and finds the optimal clusters in it. The traveling order is subsequently transformed into a data structure of contiguous subinterval heap based on which a clustering algorithm, HCLU, is designed to find the hierarchical cluster boundaries of the network without any user interaction. Experimental results on real-world and computer-generated synthetic networks show that the clustering accuracy of the proposed algorithms is higher than the baseline methods. Furthermore, they are able to produce robust hierarchical communities in various networks with low redundancy in the presence of noise. 参考文献 相似文献 引证文献

A Gene-Phenotype Network for the Laboratory Mouse and Its Implications for Systematic Phenotyping

The laboratory mouse is the pre-eminent model organism for the dissection of human disease pathways. With the advent of a comprehensive panel of gene knockouts, projects to characterise the phenotypes of all knockout lines are being initiated. The range of genotype-phenotype associations can be represented using the Mammalian Phenotype ontology. Using publicly available data annotated with this ontology we have constructed gene and phenotype networks representing these associations. These networks show a scale-free, hierarchical and modular character and community structure. They also exhibit enrichment for gene coexpression, protein-protein interactions and Gene Ontology annotation similarity. Close association between gene communities and some high-level ontology terms suggests that systematic phenotyping can provide a direct insight into underlying pathways. However some phenotypes are distributed more diffusely across gene networks, likely reflecting the pleiotropic roles of many genes. Phenotype communities show a many-to-many relationship to human disease communities, but stronger overlap at more granular levels of description. This may suggest that systematic phenotyping projects should aim for high granularity annotations to maximise their relevance to human disease.

Efficient Routing on Large Road Networks Using Hierarchical Communities

Efficient routing is essential in everyday life. Although various hierarchical algorithms exist for computing shortest paths, their heavy precomputation/storage costs and/or query costs hinder their application to large road networks. By detecting a hierarchical community structure in road networks, we develop a community-based hierarchical graph model that supports efficient route computation on large road networks. We then propose a new hierarchical routing algorithm that can significantly reduce the search space over the conventional algorithms with acceptable loss of accuracy. Experimental results on a New York road network demonstrate the performance of the algorithm.

An Efficient Path Computation Approach for Road Networks Based on Hierarchical Communities

AbstractThe problem of efficient path computation arises in several applications such as intelligent transportation and network routing. Although various algorithms exist for computing shortest paths, their heavy precomputation/storage costs and/or query costs hinder their application to real-time routing. By detecting hierarchical community structure in road networks, we develop a community-based hierarchical graph model that supports efficient path computation on large road networks. We then propose a new hierarchical routing algorithm that can significantly reduce the search space over the conventional algorithms with acceptable loss of accuracy. Finally, we evaluate our approach experimentally using a large, real-world road network to show the gain in performance.

Density-based shrinkage for revealing hierarchical and overlapping community structure in networks

The investigation of community structure in networks is an important issue in many disciplines, which still remains a challenging task. First, complex networks often show a hierarchical structure with communities embedded within other communities. Moreover, communities in the network may overlap and have noise, e.g., some nodes belonging to multiple communities and some nodes marginally connected with the communities, which are called hub and outlier, respectively. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also to identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm DenShrink. By combining the advantages of density-based clustering and modularity optimization methods, our algorithm can reveal the embedded hierarchical community structure efficiently in large-scale weighted undirected networks, and identify hubs and outliers as well. Moreover, it overcomes the resolution limit possessed by other modularity-based methods. Our experiments on the real-world and synthetic datasets show that DenShrink generates more accurate results than the baseline methods.

Eigenspaces of networks reveal the overlapping and hierarchical community structure moreprecisely

Identifying community structure is fundamental for revealing the structure–functionalityrelationship in complex networks, and spectral algorithms have been shown to be powerfulfor this purpose. In a traditional spectral algorithm, each vertex of a network is embeddedinto a spectral space by making use of the eigenvectors of the adjacency matrix orLaplacian matrix of the graph. In this paper, a novel spectral approach for revealing theoverlapping and hierarchical community structure of complex networks is proposedby not only using the eigenvalues and eigenvectors but also the properties ofeigenspaces of the networks involved. This gives us a better characterization ofcommunity. We first show that the communicability between a pair of vertices canbe rewritten in term of eigenspaces of a network. An agglomerative clusteringalgorithm is then presented to discover the hierarchical communities using thecommunicability matrix. Finally, these overlapping vertices are discovered with thecorresponding eigenspaces, based on the fact that the vertices more densely connectedamongst one another are more likely to be linked through short cycles. Comparedwith the traditional spectral algorithms, our algorithm can identify both theoverlapping and hierarchical community without increasing the time complexityO(n3), wheren is the size of the network. Furthermore, our algorithm can also distinguish theoverlapping vertices from bridges. The method is tested by applying it to somecomputer-generated and real-world networks. The experimental results indicate that ouralgorithm can reveal community structure more precisely than the traditional spectralapproaches.

Small world models for social network algorithms testing

Social networks have small-world property, hierarchical community structure, and some other properties. This paper proposes models of networks with these properties and algorithm for community structure recognition. The models are useable for testing effectiveness and efficiency of different algorithms for social network analysis.

Graphical models based hierarchical probabilistic community discovery in large-scale social networks

Real-world social networks, while disparate in nature, often comprise of a set of loose clusters (a.k.a. communities), in which members are better connected to each other than to the rest of the network. In addition, such communities are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. Discovering the complicated hierarchical community structure can gain us deeper understanding about the networks and the pertaining communities. This paper describes a hierarchical Bayesian model based scheme namely hierarchical social network-pachinko allocation model (HSN-PAM), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.