Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods.     

Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design

As a newly-developed information exchange and management platform, Building Information Modeling (BIM) is altering the way of collaboration among multi-engineers for civil engineering projects. During the BIM implementation, a large number of event logs are automatically generated and accumulated to record details of the model evolution. For knowledge discovery from huge logs, a novel BIM event log mining approach based on the dynamic social network analysis is presented to examine designers’ performance objectively, which has been verified in BIM event logs about an ongoing year-long design project. Relying on meaningful information extracted from time-stamped logs, networks on the monthly interval are built to graphically represent information and knowledge sharing among designers. Special emphasis is put on measuring designers’ influence by a defined new metric called “impact score”, which combines the k-shell method and 1-step neighbors to achieve comparatively low computational cost and high accurate ranking. Besides, an emerging machine learning algorithm named CatBoost is utilized to predict designers’ influence intelligently by learning features from both network structure and human behavior. It has been found that twelve networks can be easily distinguished into two collaborative patterns, whose characteristics in both network structures and designers’ behaviors are significantly different. The most influential designers are similar within the same group but varied from different groups. Extensive analytical results confirm that the method can potentially serve as month-by-month feedback to monitor the complex modeling process, which further supports managers to realize data-driven decision making for better leadership and work plan towards an optimized collaborative design.     

A framework for joint community detection across multiple related networks

Community detection in networks is an active area of research with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. With the rapid proliferation of online social media, it has become increasingly common for web users to have noticeable presence across multiple web sites. This raises the question whether it is possible to combine information from several networks to improve community detection. In this paper, we present a framework that identifies communities simultaneously across different networks and learns the correspondences between them. The framework is applicable to networks generated from multiple web sites as well as to those derived from heterogeneous nodes of the same web site. It also allows the incorporation of prior information about the potential relationships between the communities in different networks. Extensive experiments have been performed on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous community detection over three alternative methods, including normalized cut and matrix factorization on a single network or a bipartite graph.     

Change point detection in social networks—Critical review with experiments

Change point detection in social networks is an important element in developing the understanding of dynamic systems. This complex and growing area of research has no clear guidelines on what methods to use or in which circumstances. This paper critically discusses several possible network metrics to be used for a change point detection problem and conducts an experimental, comparative analysis using the Enron and MIT networks. Bayesian change point detection analysis is conducted on different global graph metrics (Size, Density, Average Clustering Coefficient, Average Shortest Path) as well as metrics derived from the Hierarchical and Block models (Entropy, Edge Probability, No. of Communities, Hierarchy Level Membership). The results produced the posterior probability of a change point at weekly time intervals that were analysed against ground truth change points using precision and recall measures. Results suggest that computationally heavy generative models offer only slightly better results compared to some of the global graph metrics. The simplest metrics used in the experiments, i.e. nodes and links numbers, are the recommended choice for detecting overall structural changes.     

Stochastic graph as a model for social networks

Social networks are usually modeled and represented as deterministic graphs with a set of nodes as users and edges as connection between users of networks. Due to the uncertain and dynamic nature of user behavior and human activities in social networks, their structural and behavioral parameters are time varying parameters and for this reason using deterministic graphs for modeling and analysis of behavior of users may not be appropriate. In this paper, we propose that stochastic graphs, in which weights associated with edges are random variables, may be a better candidate as a graph model for social network analysis. Thus, we first propose generalization of some network measures for stochastic graphs and then propose six learning automata based algorithms for calculating these measures under the situation that the probability distribution functions of the edge weights of the graph are unknown. Simulations on different synthetic stochastic graphs for calculating the network measures using the proposed algorithms show that in order to obtain good estimates for the network measures, the required number of samples taken from edges of the graph is significantly lower than that of standard sampling method aims to analysis of human behavior in online social networks.     

Adaptive decision-making frameworks for dynamic multi-agent organizational change

This article presents a capability called Adaptive Decision-Making Frameworks (ADMF) and shows that it can result in significantly improved system performance across run-time situation changes in a multi-agent system. Specifically, ADMF can result in improved and more robust performance compared to the use of a single static decision-making framework (DMF). The ADMF capability allows agents to dynamically adapt the DMF in which they participate to fit their run-time situation as it changes. A DMF identifies a set of agents and specifies the distribution of decision-making control and the authority to assign subtasks among these agents as they determine how a goal or set of goals should be achieved. The ADMF capability is a form of organizational adaptation and differs from previous approaches to organizational adaptation and dynamic coordination in that it is the first to allow dynamic and explicit manipulation of these DMF characteristics at run-time as variables controlling agent behavior. The approach proposed for selecting DMFs at run-time parameterizes all domain-specific knowledge as characteristics of the agents’ situation, so the approach is application-independent. The presented evaluation empirically shows that, for at least one multi-agent system, there is no one best DMF for multiple agents across run-time situational changes. Next, it motivates the further exploration of ADMF by showing that adapting DMFs to run-time variations in situation can result in improved overall system performance compared to static or random DMFs.     

Mixing local and global information for community detection in large networks

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.     

Parallel quantum-inspired evolutionary algorithms for community detection in social networks

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers.In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization.In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs.The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup.     

在线社会网络的网络结构和信息传播研究综述

随着在线社会网络的大规模应用和普及, 亟需对在线社会网络进行深入研究分析。在线社会网络的网络结构和信息传播研究是该领域中的两大研究热点和关键问题。网络结构包括关键节点、网络关系以及社团的挖掘, 通过对网络结构的分析可以掌握被分析网络中存在的社团、节点之间的关系以及关键节点等, 而这种分析对于国家及时掌握在线社会网络的舆情、公司广告在网络上投放策略的制定都具有极大的帮助。对在线社会网络信息传播的研究主要有信息传播动力模型、信息传播源和路径的发现与描绘、信息传播的最大化和最小化等, 通过对在线社会网络信息传播的研究, 人们可以对在线社会网络信息传播的影响进行预测和干预, 从而可以将信息传播的影响按照有利的方向引导。综述了在线社会网络的网络结构和信息传播的研究现状, 并对这两方面的主要研究方法及技术的优势和不足以及适用场合进行了对比分析。     

动态贝叶斯网络结构学习的依赖分析方法研究

针对现有动态贝叶斯网络结构学习方法具有低效率和低可靠性等问题,基于变量之间的基本依赖关系和依赖分析方法进行动态贝叶斯网络结构学习。建立变量之间依赖关系草图,通过条件独立行检验去除多余的边,使用碰撞识别和条件相对预测能力确定边的方向,便可得到构成动态贝叶斯网络结构的先验网和转换网。该方法在效率和可靠性方面均具有优势。     

基于攻击事件的动态网络风险评估框架

将动态网络的演化思想应用于计算机网络风险评估中,提出了基于攻击事件的动态网络风险评估框架。整个框架首先在静态物理链路的基础上构建动态访问关系网络,随后提出的Timeline算法可以利用时间特性有效地描述攻击演化趋势和发现重要攻击事件,图近似算法可以将分析过程简化为时间段近似图之间的分析,能够有效减小噪声行为的影响。此外,整体框架可以对网络段进行演化追踪和关联分析。实例分析表明,该框架具有很好的实用性,可以更好地揭示攻击者的攻击策略以及重要攻击事件间的紧密联系。     

A defence scheme against Identity Theft Attack based on multiple social networks

Recently, on-line social networking sites become more and more popular. People like to share their personal information such as their name, birthday and photos on these public sites. However, personal information could be misused by attackers. One kind of attacks called Identity Theft Attack is addressed in on-line social networking sites. After collecting the personal information of a victim, the attacker can create a fake identity to impersonate this victim and cheat the victim’s friends in order to destroy the trust relationships on the on-line social networking sites. In this paper, we propose a scheme to protect users from Identity Theft Attacks. In our work, users’ personal information can be still kept public. It means that this scheme does not violate the nature of the social networks. Compared with previous works, the proposed scheme incurs less overhead for users. Experimental results also demonstrate the practicality of the proposed scheme.     

Self-regularized causal structure discovery for trajectory-based networks

Trajectory-based networks exhibit strong heterogeneous patterns amid human behaviors. We propose a notion of causal time-varying dynamic Bayesian network (cTVDBN) to efficiently discover such patterns. While asymmetric kernels are used to make the model better adherence to causal principles, the variations of network connectivities are addressed by an adaptive over-fitting control. Compact regularization paths are obtained by approximate homotopy to make the solution tractable. In our experiments, cTVDBN structure discovery has successfully revealed the evolution of time-varying relationships in a ring road system, and provided insights for plausible road structure improvements from a traffic flow dataset.     

A dynamic methodology and associated tools to assess organizational capabilities

The assessment of organizational capabilities becomes a great challenge in extended and flexible organizations. This assessment is generally independent from the evaluation of operational results and could become isolated from the rest of the global performance system and face validity issues.     

基于忠诚度的社交网络用户发现方法

针对社交网络中提高用户的高黏性问题,提出了一种基于用户忠诚度的用户发现的算法。该算法利用双重RFM模型对用户忠诚度进行计算,挖掘出忠诚度不同分类的用户。首先,通过双重RFM模型动态计算出用户在某一时间段的消费价值与行为价值,得到用户某一时间段的忠诚度;其次,根据用户的忠诚度,确定标度曲线,利用相似度计算找到典型的忠诚用户与不忠诚用户;最后,采用基于模块度的社区发现与独立级联传播模型,发现潜在的忠诚用户与不忠诚用户。在某社交网络的微博数据集上,实现了社会性网络服务（SNS）下用户忠诚度的量化表示,获得了基于用户忠诚度的用户发现结果。实验结果表明,所提算法能够有效挖掘出基于忠诚度的用户分类,可以为社交网站针对用户的个性化推荐及营销等,提供理论支持和实用方法。     

在线社会网络的k-shell结构研究

分析研究了Twitter与You Tube两个在线社会网络的结构。用k-shell(k-壳)分解法对网络分解,并对比分析了它们的入(出)度、入(出)k-shell、以及度与k-shell之间的关系,发现它们之间有较大的差异。You Tube的入(出)度、入(出)k-shell分布均服从幂律分布,而Twitter的分布服从漂移幂律分布、指数截断的幂律分布,但它们的度与k-shell关系基本相同,都未表现出较强的相关性。此外,根据度相关系数的定义还提出k-shell相关性的定义及其计算方法,并用来刻画网络k-shell之间的同(异)配性。     

Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Centrality is one of the most important fields of social network research. To date, some centrality measures based on topological features of nodes in social networks have been proposed in which the importance of nodes is investigated from a certain point of view. Such measures are one dimensional and thus not feasible for measuring sociological features of nodes. Given that the main basis of Social Network Analysis (SNA) is related to social issues and interactions, a novel procedure is hereby proposed for developing a new centrality measure, named Sociability Centrality, based on the TOPSIS method and Genetic Algorithm (GA). This new centrality is not only based on topological features of nodes, but also a representation of their psychological and sociological features that is calculable for large size networks (e.g. online social networks) and has high correlation with the nodes' social skill questionnaire scores. Finally, efficiency of the proposed procedure for developing sociability centrality was tested via implementation on the Abrar Dataset. Our results show that this centrality measure outperforms its existing counterparts in terms of representing the social skills of nodes in a social network.     

Emerging organizational structure for knowledge-oriented teamwork using genetic algorithm

Organizations have historically sought efficiency improvements through different combinations of materials, components, production and processes to get better performance. However, in this age of the knowledge economy, the new organizational management has shifted its focus to the proper use of the knowledge of employees to create greater output and performance. There is a recent trend towards flat organizations and team-orientated structures, therefore this study will concentrate on the knowledge-oriented teamwork. To construct the fitting team structure, we solve the problem in two stages. In the first stage, we assign the proper tasks to the proper members to achieve a good match for effective usage of organizational knowledge. In the second stage, we solve the problem of insufficient knowledge within the organizational structure generated in the first stage by adjusting the positions of members to improve the mutual coordination and knowledge sharing and support.We applied a basic genetic algorithm (BGA) to solve the problems in both the stages. Five factors, such as member/task number, the number of knowledge types, the number of task types, the average complexity of each member’s knowledge types and the average complexity of task knowledge types, are considered to generate different types of problems. Computational results show that the BGA is able to find optimal knowledge matching for small-sized problems in the first stage, and that the BGA is able to improve the organizational structure generated in the first stage in order to reduce the communication cost of knowledge support among the members in the second stage.     

A videosharing social networking intervention for young adult cancer survivors

Clinicians interested in taking a proactive approach to healthy cancer survivorship might consider the use of a social networking and videosharing platform tailored specifically for young adult cancer survivors. This study examines six key factors that may influence a childhood cancer survivor’s participation in a social networking and videosharing intervention program tailored to their needs: (1) the individual’s social capital, defined as resources accessed by individuals through a broad range of social connections, (2) social support, (3) family interaction, (4) self-efficacy, (5) depression, and (6) self-reported quality of life. Fourteen healthy childhood cancer survivors participated in a social networking and videosharing intervention program, LIFECommunity, over a period of 6 months. Young adult cancer survivors with weak “bonding” social capital with other cancer survivors, little social support from friends and family, and lower family interaction participated in the social networking intervention more than those with stronger social capital and larger bases of support. The findings suggest that cancer survivors used the social network as a way to fulfill needs that were not being met in their “offline” lives. The study provides a deeper understanding of the factors that contribute to the success of social networking interventions for young cancer survivors.