Identification of Influential Users in Online Social Network: A Brief Overview

Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.

Factors influencing knowledge contribution:An empirical investigation of social networking website users

Purpose: In the Web 2.0 era,leveraging the collective power of user knowledge contributions has become an important part of the study of collective intelligence. This research aims to investigate the factors which influence knowledge contribution behavior of social networking sites(SNS) users.Design/methodology/approach: The data were obtained from an online survey of 251 social networking sites users. Structural equation modeling analysis was used to validate the proposed model.Findings: Our survey shows that the individuals' motivation for knowledge contribution,their capability of contributing knowledge,interpersonal trust and their own habits positively influence their knowledge contribution behavior,but reward does not significantly influence knowledge contribution in the online virtual community.Research limitations: Respondents of our online survey are mainly undergraduate and graduate students. A limited sample group cannot represent all of the population. A larger survey involving more SNS users may be useful.Practical implications: The results have provided some theoretical basis for promoting knowledge contribution and user viscosity.Originality/value: Few studies have investigated the impact of social influence and user habits on knowledge contribution behavior of SNS users. This study can make a theoretical contribution by examining how the social influence processes and habits affect one's knowledge contribution behavior using online communities.

Context-Oriented Multi-RAT User Association and Resource Allocation with Triple Decision in 5G Heterogeneous Networks

In the upcoming 5 G heterogeneous networks, leveraging multiple radio access technologies(RATs) shows to be a crucial issue in achieving RAT multiplexing gain to meet the explosive traffic demand. For always best connection(ABC), users tend to activate parallel transmission across all available RATs. However from a system-wide perspective, this might not be optimal given the context of network load, interference and diverse service requirements. To intelligently determine how to use these multi-RAT access resources concurrently, this paper proposes a joint multi-RAT user association and resource allocation strategy with triple decision and integrated context awareness of users and networks. A dynamic game based ant colony algorithm(GACA) is designed to simultaneously maximize the system utility and the fairness of resource allocation. Simulation results show that it's more reasonable to make multi-RAT association decision from a system-wide viewpoint than from an individual one. Compared to max-SNR based and ABC based strategies, the proposed method alleviates network congestion and optimizes resource allocation. It obtains 39%~70% performance improvement.

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.

Container Networking Performance Analysis for Large-Scale User Behavior Simulation

Accurately simulating large-scale user behavior is important to improve the similarity between the cyber range and the real network environment. The Linux Container provides a method to simulate the behavior of large-scale users under the constraints of limited physical resources. In a container-based virtualization environment, container networking is an important component. To evaluate the impact of different networking methods between the containers on the simulation performance, the typical container networking methods such as none, bridge, macvlan were analyzed, and the performance of different networking methods was evaluated according to the throughput and latency metrics. The experiments show that under the same physical resource constraints, the macvlan networking method has the best network performance, while the bridge method has the worst performance. This result provides a reference for selecting the appropriate networking method in the user behavior simulation process.

Uplink Performance Analysis in Multi-Tier Heterogeneous Cellular Networks with Power Control and Biased User Association

A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.

User growth and penetration modeling for converged heterogeneous network

To evaluate the trail potential of converged heterogeneous network （CHN） market, the logistic method for adoption modeling of CHN is used. User growth ＆ penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation, commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.

Complex field network-coded cooperation based on multi-user detection in wireless networks

Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.

Delay-Based User Association in Heterogeneous Networks with Backhaul

The Internet of things(IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when Io T applications meet heterogeneous networks(HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier Het Nets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals(UTs), and the number of base stations(BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.

User-Oriented Hybrid Opportunistic Network Design

To improve the end-to-end Successful Delivery Probability(SDP) of mobile nodes in opportunistic network,hybrid Opportunistic Network(OppNet) architecture using Fix Infrastructure Nodes(FINs) is proposed.As enhanced OppNet serving for special users,the network planning and design should take user's mobility behavior into account.We adopt collected users' mobility records,analyze users' stay pattern,and establish Message Exchange Points(MEPs) where FINs should be installed.Our solution includes MEPs detection algorithm,Maximum end-to-end Success Delivery Probability(MSDP) detection algorithm,and simulated annealing algorithm.After data preprocessing,MEPs detection algorithm detects feasible locations from users' trajectory records where not more than one user visit frequently.End to end SDPs of these feasible MEPs are evaluated using MSDP detection algorithm.To reduce the computation complexity,a simulated annealing algorithm to select optimal FIN locations in a large number of feasible MEPs is given.The evaluation experiments based on reallife trajectory dataset verify our proposal can achieve better E2 E SDP gain than "most visitors" strategy and "max residence probability" strategy.

Learning User Intention in Networked Mobile Robot Control

Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.

Energy-Efficient User Association in Heterogeneous Networks with M2M/H2H Coexistence under QoS Guarantees

In this paper, we propose an energy efficient user association scheme for uplink heterogeneous networks with machine-to-machine(M2M) and human-to-human(H2H) coexistence. A group based random access protocol is designed for massive number of machine-typecommunications(MTC) user equipments'(UEs) transmissions. A user association problem for UEs' energy efficiency maximization is formulated considering the HTC UEs' quality of service(QoS) guarantees and load balance among multiple BSs, simultaneously. A distributed iterative algorithm is developed to solve the optimization problem. In addition, the convergence of the proposed algorithm is proved. Simulation results show that our proposed scheme outperforms other schemes in terms of energy efficiency and QoS guarantees.

User Informatics Optimized Search and Retrieval-Congestion Avoidance Scheme for 4G Networks

The objective of 4G network is to provide best services to the users which in turn made the performance of existing network more critical. Further, the large traffic generated in such networks creates congestion resulting in overloading of the system. Frequent delays, loss of packets, and in addition the number of retransmission/paging also increases the computational cost of the system. This paper proposes a novel way to reduce overloading and retrieval mechanism for VLR through optimized search, based on the information of users mobility pattern (User profiles based (UPB)) to track the user. This not only improves the overall performance of the system, especially in the events when the visitor location register (VLR) is overloaded due to heavy traffic and congestion of the network. It was also established through simulation studies that the proposed UPB scheme optimizes the search and reduces the average waiting time in a queue. In addition, the provision of VLRW (waiting visitor location register) avoids the overloading of main VLR and provides a recovery/retrieval mechanism for VLR failure.

User satisfaction-based quality evaluation model and survey analysis of network information service

On the basis of user satisfaction,authors made research hypotheses by learning from relevant e-service quality evaluation models.A questionnaire survey was then conducted on some content-based websites in terms of their convenience,information quality,personalization and site aesthetics,which may affect the overall satisfaction of users.Statistical analysis was also made to build a user satisfaction-based quality evaluation system of network information service.

用户生成内容场景下角色导向图神经推荐方法

近年来互联网的飞速发展不断改变着信息的生产和传递方式,随之出现了用户使用互联网的新方式——用户生成内容(User-Generated Content,UGC).该场景中内容以传播速度快、获取成本低等优势迅速占据互联网信息传播的重要地位.不同于传统推荐场景,UGC场景下用户同时扮演生产者和消费者双重角色,这使得在构建推荐模型时既需要考虑消费者与内容之间的交互信息,也需关注内容生产者对于消费者决策的影响.因此,UGC场景下个性化推荐算法研究的关键在于如何充分挖掘消费者-内容和消费者-生产者之间的关联关系.在面向UGC场景的推荐研究中,比较有代表性的模型为CPRec,该模型虽取得一定进展,但仍存在两点不足之处.其一,在模型构建层面,未能显式建模消费者-内容与消费者-生产者之间的高阶连通关系,难以学习出高质量的节点表征.其二,在模型优化层面,无法区分每个观测数据在不同训练阶段的贡献度,将影响推荐结果的质量.为此,本文提出一种新颖的角色导向图神经推荐方法RGNRec(Role-Guided Graph Neural Recommendation)用于UGC场景的个性化排序任务.特别地,基于用户的历史行为数据与内容的创作者信息分别构建了消费者-内容交互图和消费者-生产者交互图.进一步,为了显式捕获两种交互图中的高阶连通信息,构建一种双通道线性传播模块,同时刻画了消费者兴趣与内容生产者影响的扩散过程.最终,提出设计一种自适应的正样本权重生成策略,将其融入非采样损失函数,并建立双层优化机制来学习模型的参数.本文的核心贡献包括:(1)引入双通道线性传播模块,以显式解耦出自身兴趣与内容生产者效应对于用户偏好建模的不同贡献度;(2)提出权重自适应的非采样损失函数,以解决不同观测样例在模型不同训练阶段贡献不同的问题.本文分别采用经典的和最先进的图神经网络方法作为基准,在3个UGC场景Pinterest、Recipes和Reddit下进行了实验对比.在整体推荐性能方面,无论模型精度亦或训练效率上均优于各基准方法,尤其在Precision@10指标上获得了 4.31%~17.83%的提升;然后通过消融实验验证了双通道线性传播模块和权重自适应优化机制的合理性与必要性;最后通过实验验证了本文方法在缓解数据稀疏和用户冷启动方面的优越性.

Mobile user forecast and power-law acceleration invariance of scale-free networks

This paper studies and predicts the number growth of China＇s mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well.

融合跨平台用户偏好与异质信息网络的推荐算法研究

[目的/意义]本文基于跨平台用户的异构大数据,提出一种融合跨平台用户偏好与异质信息网络的推荐算法(CPHAR),对于缓解个性化推荐的稀疏性和冷启动问题具有重要意义。[方法/过程]首先,根据跨平台用户信息构建核心兴趣朋友圈,使用卷积神经网络和自注意力机制捕捉用户在源平台和目标平台中的信息偏好特征;其次,根据核心兴趣网络以及推荐项目之间的关系构建异质信息网络,使用异质图注意力网络模型进行特征聚合;最后,将以上特征嵌入改进后的矩阵分解模型,计算推荐得分。[结果/结论]模型在自主构建的4个跨平台数据集中均表现出优越的性能,本文不仅弥补了推荐领域中跨平台多属性和细粒度数据集的空缺,而且通过引入跨平台特征进一步完善了推荐系统相关的理论与方法体系。

基于区块链的城市智能交通网络均衡分配方法

为了提高城市智能交通网络的应用效率,有效分配网络中存在的资源,同时保证网络资源分配的均衡度。因此,提出区块链技术下城市智能交通网络均衡分配方法。通过建立城市智能交通网络模型,分析用户在交通网络中的资源需求。根据用户的需求,采用区块链技术分配网络中的资源,通过引入椭圆曲线数字签名算法,对需求和资源的发布信息加密处理,并根据资源需求与资源供应之间的匹配,完成城市智能交通网络资源的均衡分配。实验结果表明,所提方法的用户吞吐率高,且Jain公平指数在0.8以上,表明所提方法的资源分配合理性高。

基于偏好感知的去噪图卷积网络社交推荐

协同过滤推荐通常面临用户-项目交互数据稀疏的挑战,社交推荐引入用户社交关系来缓解数据稀疏性问题。多数基于图神经网络(GNN)的社交推荐系统在消息传递过程中无法根据用户偏好聚合高阶邻居信息,造成嵌入表示过平滑和噪声问题。针对上述问题,提出一种基于偏好感知的去噪图卷积网络的社交推荐模型PD-GCN。使用无监督学习将具有相似偏好的用户分配到用户-项目交互子图和社交子图,在子图中进行更高阶的图卷积运算,缓解了现有模型的过平滑问题。从全局和局部的角度出发,通过考虑相同偏好用户节点的特征相似度和邻域节点偏好分布多样性识别并去除噪声节点,增强模型对用户-项目交互和社交关系噪声的鲁棒性。在LastFM、Ciao、Yelp 3个公共数据集上的实验结果表明,PD-GCN模型在召回率和归一化折损累计增益两个指标上相较于其他主流模型表现出更优的性能,验证了PD-GCN模型的有效性。

基于象限分割的迟滞软切换VLC-WiFi接入选择算法

针对解决无线网络频谱匮乏、用户频繁切换问题,本文提出一种基于象限分割的VLC-WiFi异构接入与迟滞软切换算法。依照三维坐标系将室内分割为4部分建立用户速率模型,获取用户速率,建立遮挡模型模拟VLC链路被遮挡情景。用户依据所在位置、象限、遮挡及速率等参数执行接入操作;切换依据迟滞思想将向内切换与向外切换阈值差异化,使切换存在弹性空间从而抑制乒乓效应。在5 m×5 m×5 m室内空间内经过多次实验表明,提出的室内VLC-WiFi异构组网方案水平切换次数降低43.8%,遮挡及垂直切换数降低45.3%,乒乓次数降低52.27%,吞吐量比传统算法提升约5.84%。该算法可以显著降低切换与乒乓次数。因此本文为室内异构网络通信研究提供理论依据。