Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.     

Local Community Detection Using Link Similarity

Exploring local community structure is an appealing problem that has drawn much recent attention in the area of social network analysis.As the complete information of network is often difficult to obtain,such as networks of web pages,research papers and Facebook users,people can only detect community structure from a certain source vertex with limited knowledge of the entire graph.The existing approaches do well in measuring the community quality,but they are largely dependent on source vertex and putting too strict policy in agglomerating new vertices.Moreover,they have predefined parameters which are difficult to obtain.This paper proposes a method to find local community structure by analyzing link similarity between the community and the vertex.Inspired by the fact that elements in the same community are more likely to share common links,we explore community structure heuristically by giving priority to vertices which have a high link similarity with the community.A three-phase process is also used for the sake of improving quality of community structure.Experimental results prove that our method performs effectively not only in computer-generated graphs but also in real-world graphs.     

Finding and evaluating the community structure in semantic peer-to-peer overlay networks

Recently much attention has been paid to semantic overlay networks for information retrieval in large scale peer-to-peer networks,and much research work on semantic overlay protocols and searching algorithms has been done and the results indicate that semantic overlay is efficient for content searching in peer-to-peer networks.However,very limited work has been done to analyze and evaluate the characteristics of semantic overlay networks.In this paper we identify a natural property of semantic overlay networks,the community structure.We propose a mathematical model to evaluate the property of community structure of semantic P2P overlay networks.A heuristic algorithm is designed to optimize the community structure.Using the evaluation model we compare the SemreX semantic overlay with the Gnutella network.Results demonstrate that a SemreX overlay network has the distinctive community structure feature,while a Gnutella-like network does not.We also simulate a simple flooding protocol in both overlays to show that the overlay with community structure is more efficient for content searching.     

Influence maximization with limit cost in social network

Social networking service (SNS) applications are changing the way information spreads in online communities. As real social relationships are projected into SNS applications, word of mouth has been an important factor in the information spreading processes of those applications. By assuming each user needs a cost to accept some specific information, this paper studies the initial "seed user" selection strategy to maximize information spreading in a social network with a cost budget. The main contributions of this paper are: 1) proposing a graphic SEIR model (gSEIR) by extending the epidemic compartmental model to simulate the dynamic information spreading process between individuals in the social network; 2) proposing a formal definition for the influence maximization problem with limit cost (IMLC) in social networks, and proving that this problem can be transformed to the weighted set-cover problem (WSCP) and thus is NP-Complete; 3) providing four different greedy algorithms to solve the IMLC problem; 4) proposing a heuristic algorithm based on the method of Lagrange multipliers (HILR) for the same problem; 5) providing two parts of experiments to test the proposed models and algorithms in this paper. In the first part, we verify that gSEIR can generate similar macro-behavior as an SIR model for the information spreading process in an online community by combining the micro-behaviors of all the users in that community, and that gSEIR can also simulate the dynamic change process of the statuses of all the individuals in the corresponding social networks during the information spreading process. In the second part, by applying the simulation result from gSEIR as the prediction of information spreading in the given social network, we test the effectiveness and efficiency of all provided algorithms to solve the influence maximization problem with cost limit. The result show that the heuristic algorithm HILR is the best for the IMLC problem.     

Direct heuristic dynamic programming based on an improved PID neural network

In this paper,an improved PID-neural network(IPIDNN) structure is proposed and applied to the critic and action networks of direct heuristic dynamic programming(DHDP).As one of online learning algorithm of approximate dynamic programming(ADP),DHDP has demonstrated its applicability to large state and control problems.Theoretically, the DHDP algorithm requires access to full state feedback in order to obtain solutions to the Bellman optimality equation. Unfortunately,it is not always possible to access all the states in a real system.This paper proposes a solution by suggesting an IPIDNN configuration to construct the critic and action networks to achieve an output feedback control.Since this structure can estimate the integrals and derivatives of measurable outputs,more system states are utilized and thus better control performance are expected.Compared with traditional PIDNN,this configuration is flexible and easy to expand. Based on this structure,a gradient decent algorithm for this IPIDNN-based DHDP is presented.Convergence issues are addressed within a single learning time step and for the entire learning process.Some important insights are provided to guide the implementation of the algorithm.The proposed learning controller has been applied to a cart-pole system to validate the effectiveness of the structure and the algorithm.     

A novel decomposition and coordination algorithm for complex networks and its application to power grids

To analyze and control complex networks effectively, this paper puts forward a new kind of scheme, which takes control separately in each area and can achieve the network＇s coordinated optimality. The proposed algorithm is made up of two parts： the first part decomposes the network into several independent areas based on community structure and decouples the information flow and control power among areas; the second part selects the center nodes from each area with the help of the control centrality index. As long as the status of center nodes is kept on a satisfactory level in each area, the whole system is under effective control. Finally, the algorithm is applied to power grids, and the simulations prove its effectiveness.     

A Heuristic Clustering Algorithm for Mining Communities in Signed Networks

Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks. So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures. Finally, it is insensitive to its built-in parameters and requires no prior knowledge.     

Finding Communities by Decomposing and Embedding Heterogeneous Information Network

Community discovery is an important task in social network analysis.However,most existing methods for community discovery rely on the topological structure alone.These methods ignore the rich information available in the content data.In order to solve this issue,in this paper,we present a community discovery method based on heterogeneous information network decomposition and embedding.Unlike traditional methods,our method takes into account topology,node content and edge content,which can supply abundant evidence for community discovery.First,an embedding-based similarity evaluation method is proposed,which decomposes the heterogeneous information network into several subnetworks,and extracts their potential deep representation to evaluate the similarities between nodes.Second,a bottom-up community discovery algorithm is proposed.Via leader nodes selection,initial community generation,and community expansion,communities can be found more efficiently.Third,some incremental maintenance strategies for the changes of networks are proposed.We conduct experimental studies based on three real-world social networks.Experiments demonstrate the effectiveness and the efficiency of our proposed method.Compared with the traditional methods,our method improves normalized mutual information(NMI)and the modularity by an average of 12%and 37%respectively.     

Community structure detection in social networks based on dictionary learning

Discovering community structures is a fundamental problem concerning how to understand the topology and the functions of complex network. In this paper, we propose how to apply dictionary learning algorithm to community structure detection. We present a new dictionary learning algorithm and systematically compare it with other state-of-the-art models/algorithms. The results show that the proposed algorithm is highly effectively at finding the community structures in both synthetic datasets, including three types of data structures, and real world networks coming from different areas.     

An autonomy-oriented computing approach to community mining in distributed and dynamic networks

A network community refers to a special type of network structure that contains a group of nodes connected based on certain relationships or similar properties. Our ability to mine communities hidden inside networks will readily enable us to effectively understand and exploit such networks. So far, various methods and algorithms have been developed to perform the task of community mining, where it is often required that the networks are processed in a centralized manner, and their structures will not dynamically change. However, in the real world, many applications involve distributed and dynamically evolving networks, in which resources and controls are not only decentralized but also updated frequently. It would be difficult for the existing methods to deal with these types of networks since their global topological representations are either not available or too hard to obtain due to their huge size, decentralization, and/or dynamic updates. The aim of our work is to address the problem of mining communities from a distributed and dynamic network. It differs from the previous ones in that here we introduce the notion of self-organizing agent networks, and provide an autonomy-oriented computing (AOC) approach to distributed and incremental mining of network communities. The AOC-based method utilizes reactive agents that can collectively detect and update community structures in a distributed and dynamically evolving network, based only on their local views and interactions. While providing detailed formulations, we present the results of our systematic validations using real-world benchmark networks as well as synthetic networks that include a distributed intelligent Portable Digital Assistant (iPDA) network example.     

基于动态有噪网络的增量社团发现算法

在动态社会网络中,诸如垃圾邮件之类的噪声会影响网络的稳定性,导致其社团结构难以被准确发现。针对该问题,提出一种采用增量结构的社团发现算法。利用相对熵处理噪声,通过改进的增量算法发现社团结构。实验结果表明,该算法针对不同动态社会网络的发现性能均优于传统动态社团发现算法,其模块度可达到0.8左右,互信息值变化也较平稳,可有效避免噪声对算法性能的影响。     

动态加权网络中的演化社区发现算法研究

在动态网络中发现社区结构是一个非常复杂而有意义的过程,可以更好地观察和分析网络的演化情况。针对动态加权网络中的社区发现问题,提出了一种结合历史网络社区结构的算法,叫做动态加权网络中的演化社区发现算法（ECDA）。该算法分为两步：结合历史社区和网络结构信息,计算当前时间跳的输入矩阵;然后通过该输入矩阵计算得到结合历史时间跳信息的社区划分结果。该算法有以下优点：可以自动发现动态加权网络中每个时间跳的社区结构;对网络结构的变化和社区结构的变化具有较高的敏锐性。在人工数据集和真实数据集中进行了实验,实验结果证明该算法可以有效地发现动态加权网络中的社区结构,与其他算法相比具有较好的竞争力。     

基于联合矩阵分解的动态异质网络社区发现方法

动态网络的社区发现是目前复杂网络分析领域的重要研究内容,然而现有动态网络社区发现方法主要针对同质网络,当网络包含多种异质信息时,现有方法不再适用。针对这个问题,本文提出了一个基于联合矩阵分解的动态异质网络社区发现方法,首先计算动态异质网路中各个快照图的拓扑相似度矩阵和多关系相似度矩阵,其次利用时序联合非负矩阵分解方法,约束各个时刻快照图的社区划分,最后在真实网络数据集上的实验结果表明,该算法可以有效检测出动态异质网络中潜在的社区结构。     

Incremental K-clique clustering in dynamic social networks

Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.     

基于桥系数的分裂社区检测算法研究

研究社区结构有助于揭示网络结构和功能之间的关系,而社区检测是社区结构研究的基础和核心。该文定义了一种聚集度桥系数,将其应用到社区检测中,设计出一种分裂社区检测方法,包括分裂和合并两个算法。分裂算法使用桥系数识别社区间边,通过迭代删除社区间边分解网络,从而发现网络中的社区结构;合并算法根据社区连接强度合并社区,可以揭示社区结构中的分层嵌套的现象。在六个社会网络数据集上的实验表明,本文算法可以有效的将网络分裂为有意义的社区,并且准确性接近或超过经典的社区检测算法。     

一种基于增量式谱聚类的动态社区自适应发现算法

针对当前复杂网络动态社区发现的热点问题, 提出一种面向静态网络社区发现的链接相关线性谱聚类算法, 并在此基础上提出一种基于增量式谱聚类的动态社区自适应发现算法. 动态社区发现算法引入归一化图形拉普拉斯矩阵呈现复杂网络节点之间的关 系,采用拉普拉斯本征映射将节点投影到k维欧式空间.为解决离群节点影响谱聚类的效果和启发式确定复杂网络社区数量的问题, 利用提出的链接相关线性谱聚类算法发现初始时间片的社区结构, 使发现社区的过程能够以较低的时间开销自适应地挖掘复杂网络社区结构. 此后, 对于后续相邻的时间片, 提出的增量式谱聚类算法以前一时间片聚类获得的社区特征为基础, 通过调整链接相关线性谱聚类算法实现对后一时间片的增量聚类, 以达到自适应地发现复杂网络动态社区的目的. 在多个数据集的实验表明, 提出的链接相关线性谱聚类算法能够有效地检测出复杂网络中的社区结构以及基于 增量式谱聚类的动态社区自适应发现算法能够有效地挖掘网络中动态社区的演化过程.     

基于权重信息挖掘社会网络中的隐含社团

社团结构是一种普遍存在于各类真实网络中的结构特性.挖掘网络的社团结构对于理解网络的功能与行为有着重要作用.然而,现有的各种社团挖掘算法仅仅基于网络拓扑结构信息,而忽视了蕴涵于真实社会网络边权信息中丰富的语义信息.目前普遍使用的基于模块性最大化的社团挖掘算法倾向于将小社团合并,这使得语义上丰富的小社团容易湮灭于基于拓扑结构信息所挖掘出的大社团中.而挖掘出这些隐含于大社团中的有着丰富语义内涵的小社团对于加深社会网络语义层面的理解有着重要作用.为此,提出一个接近线性复杂度的有权网络社团挖掘算法.通过充分利用权重信息,算法可以将社会网络划分为富含语义信息的粒度较细且相对较小的隐含社团.通过对基于DBLP作者合作网络的实证分析,证实了新算法的有效性和高效性.     

Incrementally mining high utility patterns based on pre-large concept

In traditional association rule mining, most algorithms are designed to discover frequent itemsets from a binary database. Utility mining was thus proposed to measure the utility values of purchased items for revealing high utility itemsets from a quantitative database. In the past, a two-phase high utility mining algorithm was thus proposed for efficiently discovering high utility itemsets from a quantitative database. In dynamic data mining, transactions may be inserted, deleted, or modified from a database. In this case, a batch mining procedure must rescan the whole updated database to maintain the up-to-date information. Designing an efficient approach for handling dynamic databases is thus a critical research issue in utility mining. In this paper, an incremental mining algorithm is proposed for efficiently maintaining discovered high utility itemsets based on pre-large concepts. Itemsets are first partitioned into three parts according to whether they have large (high), pre-large, or small transaction-weighted utilization in the original database and in inserted transactions. Individual procedures are then executed for each part. Experimental results show that the proposed incremental high utility mining algorithm outperforms existing algorithms.     

一种基于增广网络的快速微博社区检测算法

微博是当前最流行的在线社交媒体之一,有效地检测出微博用户的社区结构,能够帮助人们理解微博社交网络的结构和用户的行为特征,从而为用户提供个性化的服务。然而,现有社区检测算法大多只考虑社交网络节点之间的直接链接关系,忽略节点自身的内容特征。针对此问题,提出一种基于增广网络的快速微博社区检测算法。该算法通过融合社交网络的链接信息以及用户在微博上所发布的博文内容信息构建增广网络,然后以模块度为目标函数快速挖掘增广网络中的主题社区。通过真实微博社交网络的实验表明,提出的算法能够高效地检测出社交网络的主题社区。