Detecting communities and their evolutions in dynamic social networks—a Bayesian approach

Although a large body of work is devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. In this paper, we propose a dynamic stochastic block model for finding communities and their evolution in a dynamic social network. The proposed model captures the evolution of communities by explicitly modeling the transition of community memberships for individual nodes in the network. Unlike many existing approaches for modeling social networks that estimate parameters by their most likely values (i.e., point estimation), in this study, we employ a Bayesian treatment for parameter estimation that computes the posterior distributions for all the unknown parameters. This Bayesian treatment allows us to capture the uncertainty in parameter values and therefore is more robust to data noise than point estimation. In addition, an efficient algorithm is developed for Bayesian inference to handle large sparse social networks. Extensive experimental studies based on both synthetic data and real-life data demonstrate that our model achieves higher accuracy and reveals more insights in the data than several state-of-the-art algorithms.     

An adaptive group decision making framework: Individual and local world opinion based opinion dynamics

Opinion dynamics (OD) models, which simulate individuals’ opinion evolution process on social network to analyze the final state of opinion distribution in a group, usually differ from each other due to the differences in social network evolution rules and opinion evolution rules. However, most existing social network evolution rules and opinion evolution rules usually cannot characterize the comprehensive influence of key factors such as neighbors and opinion differences in social relationships. To fully consider the properties of social network evolution and improve the efficiency of consensus reaching process in group decision making, this paper introduces the concept of local world opinion derived from individuals’ common friends, and then proposes an individual and local world opinion-based OD model. In the proposed model, social network evolution is jointly determined by the distance between individual opinions and network structure similarity. The pair of individuals with the largest consensus improvement space are then suggested to adjust their opinions by using an adaptive individual opinion adjustment mechanism. Finally, detailed simulation results are provided to demonstrate the convergence of the proposed model and analyze different parameters’ effects on the stabilized time steps and the number of stable state opinion clusters.     

不良舆情话题在传播节点网络模型的演化规律研究

不良网络舆情是网络时代的一种重要舆论形态,反映了公众对突发事件的情绪表达,具有很强的社会影响力,需要及时处置和正确引导。本文分析了突发事件网络舆情的演化动力和影响因素,对不同阶段的舆情传播演化规律进行研究,并同步构建了网络舆情的传播模型,从而为做好舆情防控工作提供参考。     

Visual Exploration of Financial Data with Incremental Domain Knowledge

Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation-wide aggregate data. Our solution is coupled with a pipeline for the generation of firm-to-firm financial transaction networks, fusing information about individual firms with sector-to-sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight-based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.     

Distributed Apprenticeship in Online Communities

Supporting learning in online communities is an important direction for the future of human–computer interaction as people increasingly leverage social technologies to support professional growth and development. However, few have studied how people leverage the socio-technical affordances of online informal workplace communities to develop professional skills in the absence of dedicated expert guidance. We draw from theories of apprenticeship to introduce an emergent theory of distributed apprenticeship, which outlines how community expectations of transparency and mutual support allow for instruction to be directed by a distributed network of nonexperts. We develop distributed apprenticeship through a qualitative study of crowdfunding entrepreneurs, where novices leverage social interactions with community members to develop a wide range of entrepreneurial skills. We then generalize distributed apprenticeship to other workplace contexts and provide design implications for online communities where people develop professional skills with minimal dedicated formal guidance.     

AN EPIDEMIOLOGICAL MODEL OF KNOWLEDGE DIFFUSION IN VIRTUAL LEARNING NETWORKS: AN APPLICATION TO LEARNING STATISTICS IN MEDICINE

ABSTRACT

Some authors claim that the genetic metaphor can be used for describing intergenerational cultural transmission. In analogy with genes, memes can be conceived as cultural units that can be transmitted from one generation to another. A variant of this metaphor conceives cultural transmission as a sort of contagion within a social network of individuals.In this respect, distance learning networks provide a valuable tool for analyzing the spreading of information in virtual communities. In this article, the time evolution of the student's applications to an interactive distance learning platform was analysed. The platform DVLN was developed at the University of Naples and allows tracking all the students' interactions with the system.

The diffusion of the innovation among the students of the Course of Medical Statistics was modelled by means of a system of differential Equations where the diffusion of the innovation adoption depends on the contact rate between two types of students: the innovators and the potential imitators.

Furthermore, by means of k-means cluster analysis of the interactions of the students with the system, three learning styles, i.e. lurkers, socializers, utilitarists, were found. An attempt to explain the macroscopic spreading of innovation adoption on the basis of individual microscopic decisions to accept or refute the innovation was carried out.

The authors claim that the representation of the learning process as a temporal dynamics of a network of cognitive agents opens new perspectives to the scientific analysis of the learning process in human groups.     

An active opinion dynamics model: the gap between the voting result and group opinion

Originally developed to simulate the evolution of public opinion, opinion dynamics models have also been successfully applied to market pricing and advertising. However, passive interactions initiated by locational or social relationships in these models are insufficient to characterize purposeful behaviours such as canvassing or trading, where people are driven by their specific intrinsic motivations. Here, we propose an active model in which people tend to communicate with someone who is more likely to be an ally and game theoretically decide whether to interact. Model simulations highlight the macroscopic development of opinion evolution, showing the ubiquitous gap between people’s voting result and their collective opinion, and how it narrows with the stabilization of opinion evolution. Our results help explain why group opinion rarely reverses its initial stance and the significance of a level of inclusiveness that is neither too high nor too low. Additionally, we find and attest to the probability distribution of group opinion change, which contributes to predicting how much the collective opinion of a group will change after full discussion.     

D2SNet: Dynamics of diffusion and dynamic human behaviour in social networks

In this paper, we present an original and formal framework, the D2SNet model designed to combine both the social network evolution and the diffusion dynamics among individuals. We have conducted experiments on three social networks that show identical characteristics as real social networks. A formal definition of the model is provided and we describe its implementation in a simulation tool. We represent human behaviors and information dissemination strategies by standard and synthetic scheme. In a first step, we study the impact of network growing strategies only and we highlight important parameters such as the evolution speed and mainly the kind of strategies that favour or not the spread. Then we study a more complete evolution strategy that involves link creation and deletion. We provide a deep analysis on the impact of each parameter such as evolution speed, creation and deletion probabilities and dynamic human behaviors on the diffusion amplitude and coverage. Our study gives a novel and useful insight in the diffusion process in a dynamic context.     

网络社区中的意见领袖特征分析

本文通过社会网络分析方法识别网络社区中的意见领袖.首先对意见领袖存在的人际关系网络结构特征进行分析,对比论坛、博客和问答网络之间的区别,提出基于无向、有权重网络模型更能真实准确地识别意见领袖.并基于该网络模型研究和分析了网络论坛结构特征,通过测量其小世界和无标度的复杂网络特征,定量分析意见领袖存在的社会性根源.其次提出...     

复杂网络演化的舆论动力学模型及仿真分析

针对舆论传播过程中复杂动力学演化问题,提出一种基于传播动力学的舆论动态演化模型。首先,构建舆论及舆论演化模型,通过方程变换求出静态解;其次,引入Fokker-Planck方程对舆论演化渐近行为进行分析,得到稳态解决方案并求解,构建复杂网络与模型的关联并提出仿真研究实验目的;最后,通过对舆论演化模型及引入Fokker-Planck方程的舆论意见模型进行仿真分析,并以真实微博舆论数据为例进行实证分析,研究舆论在复杂网络中传播和演化的实质。实验结果表明舆论网络演化渐近行为与度分布相一致,网络舆论传播中的连接方式会受到节点意见影响,模型能有效描述微博舆论传播网络形成和演化过程的动力学行为。     

Overlapping community detection in social networks using coalitional games

Community detection is a significant research problem in various fields such as computer science, sociology and biology. The singular characteristic of communities in social networks is the multimembership of a node resulting in overlapping communities. But dealing with the problem of overlapping community detection is computationally expensive. The evolution of communities in social networks happens due to the self-interest of the nodes. The nodes of the social network acts as self-interested players, who wish to maximize their benefit through interactions in due course of community formation. Game theory provides a systematic framework tox capture the interactions between these selfish players in the form of games. In this paper, we propose a Community Detection Game (CDG) that works under the cooperative game framework. We develop a greedy community detection algorithm that employs Shapley value mechanism and majority voting mechanism in order to disclose the underlying community structure of the given network. Extensive experimental evaluation on synthetic and real-world network datasets demonstrates the effectiveness of CDG algorithm over the state-of-the-art algorithms.     

Consensus in a social network with two principals

This paper considers a model of opinion dynamics in a social network with two principals, in which the members may affect the opinions of each other and their opinions evolve according to a time-homogeneous Markov chain. We study the existence of a consensus in this network for two types of influence models, namely, when the principals may or may not affect the opinions of each other directly. In addition, we find the values of social network parameters under which a consensus is reached. For the cases without a consensus in its standard definition, we introduce the notion of a consensus of the majority and find the parameter values under which it is reached. Two numerical examples illustrate the obtained theoretical results.     

DANCer: dynamic attributed networks with community structure generation

Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments.     

Visualizing the evolution of a web-based social network

Weblogs are dynamic websites updated via easy-to-use content management systems and organized as a set of chronologically ordered stories, frequently built around a link or including links to other weblogs. Since they are managed by individuals, their links tend to mirror or, in some cases, establish new types of social relations, thereby creating a social network. Studying the evolution of this network allows the discovery of emerging social structures and their growth trends. In this paper, we demonstrate the advantages of using the self-organizing maps (SOM) to visualize the evolution of a social network formed by a set of blogs, from their beginning to their current state. By observing the position a weblog is mapped to, it is easy to see what communities it belongs to nowadays, and how and when it became a part of those communities. The proposed procedure gives some insight on how communities are formed and have evolved. In this study, we apply this method to Blogalia, a blog-hosting site from which we have obtained a complete set of data and, by using SOM projections, we have drawn some conclusions on what drives the evolution of its implicit social network.     

个体间相互影响的网络舆情演变模型

提出了一个社会网络中舆论形成的演化模型,模型考虑了网络中个体受其邻居影响的概率。假设个体A受到其他邻居影响的概率为α（A）,并且所有k度个体具有相同的受影响概率为α（k）,其中k是某个体邻居的个数。证明了如果概率α的分布满足对所有k满足α（k）=kp_kc,那么持某种舆论个体的人数比例是一个鞅,即数学期望是一个常数。本模型有助于衡量某给定社会网络中舆论传播的快慢程度。     

Markov-network based latent link analysis for community detection in social behavioral interactions

How to represent and discover social links from the perspective of implied behaviors, in particular latent links, is critical for social media analysis. In this paper, we discuss latent link analysis for community detection in social behavioral interactions. We adopt Markov network (MN) as the framework and propose the algorithm to discover latent links among social objects implied in their behavioral interactions without regard for the topological structures of social networks. First, starting from the frequent itemsets of the behavioral interactions, we propose the algorithm to construct the item-association Markov network (IAMN), which establishes the inherent relationship between frequent itemset and MN. Then, we propose the algorithm to detect communities by incorporating the concepts of k-clique and k-nearest neighbor set, as the typical application of the constructed IAMN Experimental results show the effectiveness and efficiency of the method proposed in this paper.     

Emerging opinion leaders in crowd unfollow crisis: a case study of mobile brands in Twitter

Recent years have witnessed the increasing consequences of social networks. For companies, followers in social networks are wealthy because they help to cultivate brand popularity and build well-targeted communities. However, people unfollow sometimes, which indicates a sign of breaking relationships and even losing potential customers. In spite of the fact that unfollow behavior happens frequently, we perceieve that something might be wrong when a unusual large number of unfollows happen simultaneously within a specific window, termed as crowd unfollow. To this end, in this paper we study on the problem of emerging opinion leaders in crowd unfollow and hypothesize that opinion leaders have an impact on the unfollow decision of others. Specifically, given a target brand, we propose a framework to detect crowd unfollow event in real-time and discover opinion leaders within a unfollow social network. Experiments are conducted on the Twitter accounts of three mobile brands. From the empirical results, we have two observations: (1) crowd unfollow could be either durable long-last or short-lived peak shaped; (2) opinion leaders could emerge in crowd unfollow event, which leads to crowd unfollow crisis.     

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.     

Modelling majority and expert influences on opinion formation in online social networks

Two most important social influences that shape the opinion formation process are: (i) the majority influence caused by the existence of a large group of people sharing similar opinions and (ii) the expert influence originated from the presence of experts in a social group. When these two effects contradict each other in real life, they may pull the public opinions towards their respective directions. Existing models on opinion formation utilised the idea of expertise levels in conjunction with the expressed opinions of the agents to encapsulate the expert effect. However, they have disregarded the explicit consideration of the majority effect, and thereby failed to capture the concurrent and combined impact of these two influences on opinion evolution. To represent the majority and expert impacts, we explicitly use the concept of opinion consistency and expertise level consistency respectively in an innovative way by capitalizing the notion of entropy in measuring the homogeneity of a group. Consequently, our model successfully captures the opinion dynamics under the concomitant influence of majority and expert. We validate the efficacy of our model in capturing opinion dynamics in a real world scenario using the opinion evolution traces collected from a widely used online social network (OSN) platform. Moreover, simulation results reveal the impact of the aforementioned effects, and confirm that our model can properly capture the consensus, polarization and fragmentation properties of public opinion. Our model is also compared with some recent models to evaluate its performance in both real world and simulated environments.     

Optimizing consensus reaching in the hybrid opinion dynamics in a social network•

Hybrid opinion dynamics which involves two types of individuals (i.e., leaders and followers) communicate in real time and share opinions and knowledge have been widely used in diverse applications. In real applications of hybrid opinion dynamics, one of the main demands is how to manage a consensus among individuals. This paper aims at proposing a novel consensus reaching strategy for the hybrid opinion dynamics in a social network. Firstly, we give the network partition algorithm to divide the social network into sub-network, and introduce Floyd algorithm to calculate the shortest path between any two individuals, which can provide the assistance for determining the weights among individuals. On this basis, we present the hybrid opinion dynamics model. Next, we develop the consensus reaching model with minimum adjustments (i.e. CRMD model) in hybrid opinion dynamics, and discuss some the properties of the CRMD model. Furthermore, the detailed numerical and simulation analysis are conducted to illustrate the effectiveness of this CRMD model. The simulation results show the CRMD model has the distinct advantages over other consensus strategies. Thus, the CRMD model is helpful to manage and control the public opinions for the government and enterprise.