Correlation in mobile call networks from structure perspective

Researchers have done considerable work on the structure of social network recently, but mostly neglected the correlation between two connected nodes. In this paper, our primary goal is to acquire users’ structural properties in mobile call networks. We take a novel perspective-structure correlation between two connected users perspective to study the structural properties. To investigate the structural properties in static and dynamic mobile call networks, we define some metrics which are based on the clique size vectors of mobile call users. By exploring several real-world mobile call networks, which contain hundreds of thousands of mobile call users respectively, we find that people tend to communicate with the one who has a similar structure in static mobile call networks. Moreover, It is found that the connected people have similar structural changes on the whole in dynamicmobile call networks, and the structures of some two connected persons both have growing or shrinking trends. We use a visualization toolkit to give a view of the growing or shrinking scenarios temporally.     

Multidimensional views on mobile call network

Recent studies on social network have spurred significant interests in human behaviors. Nowadays, various kinds of interpersonal human interactions, from mobile calls to emails, provide particular avenues to explore the inherent properties of communication patterns. In this article, we present a comprehensive study on a massive anonymous call records obtained from a major mobile service operator. The important difference laid in our work and previous mainly topological analyses is that we report on multiple aspects of the dataset. By investigating the calls of the users, we find out that most calls tend to last within one minute. Call duration between two females is much longer than that of two males. But calls of males generally involve more stations than that of female, indicating a larger mobile range of the males. We also observed that people tend to communicate more with each other when they share similar characters. Besides, the network is well-connected and robust to random attack. We also demonstrate that the close-knit sub-groups with little discrepancy in the characteristics of its involved users usually evoke more calls. Another interesting discovery is that call behaviors among people between workdays and weekends is obviously distinct. Generally speaking, the goal that we research on call network through multidimensional analyses is to uncover the intricate patterns of human communications and put up reasonable insights into future service intelligence.     

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。     

神经网络结构动态优化设计的分析与展望

阐述了神经网络结构设计对神经网络性能的影响. 介绍了动态结构神经网络, 尤其是增长型和修剪型神经网络研究的发展过程, 分析了动态设计方法研究在计算能力、学习理论和网络的稳定性等方面取得的成果. 最后对神经网络动态设计的研究进行总结, 给出了神经网络结构动态设计研究的发展趋势.     

适合于移动多媒体网络的呼叫接纳控制算法

移动多媒体网络应该能够同时支持传统的数据业务和实时交互式多媒体业务,并能够为用户提供QoS保证.在无线移动网中提供QoS保证,呼叫接纳控制扮演着重要的角色.通过对呼叫接纳算法中资源预留方案进行了分析总结,提出了一种适合于移动多媒体网络的自适应呼叫接纳控制算法.     

基于移动社交网络的空间资源利用分析系统*

提出并设计了一个办公环境下室内空间资源利用分析系统。该系统以基于位置的移动社交网络获取用户的位置数据,并以此产生用户活动热度分布图。以热度图形式展示一段时间内各空间处的人员密度高低,进而反映室内空间资源的利用情况。通过对会议室的实际利用情况与对应热度图比较,结果的一致性证明了该方案和系统的有效性及可靠性。     

移动Ad hoc网络的黑洞攻击研究*

移动Ad hoc网络(MANET)是一个极具实际应用价值的研究领域,由于其自身的开放媒质、动态拓扑、分布式协作和受限能力等特点,MANET极易受到攻击,其安全问题正受到越来越广泛的重视。路由在整个网络安全中扮演重要角色,首先综合论述一种路由攻击类型——黑洞问题的研究概况,然后剖析黑洞问题的基本定义、功能结构和工作原理,归纳总结目前防范黑洞问题的最新技术,最后给出针对黑洞问题研究的若干热点问题与展望。     

一种个性化移动社交网络轨迹隐私保护方案*

针对移动社交网络迅猛发展带来的发布轨迹隐私泄露问题,提出了一种个性化的轨迹保护方案。根据个体个性化的隐私保护需求差异,对不同个体采用了不同的保护准则,这样可以解决传统隐私保护下“过度保护”及轨迹效用低等问题。给出k敏感轨迹匿名和(k,p)敏感轨迹匿名等重要的隐私保护定义,并利用Trie树的构造、剪枝、重构等技术实现了个体的个性化隐私保护。最后,通过在真实数据集上的实验分析,证明该个性化方案比现存隐私保护方案在轨迹位置损失率方面性能优,计算延时较低和效率高。     

大规模移动自组网安全群组通信研究

移动自组网中的安全群组通信需要处理群组成员关系的动态性、成员位置的动态性、以及网络分区和合并等情况。如何高效地处理这些情况是移动自组网安全群组通信中要解决的重要问题。针对成员关系动态性、成员位置动态性和网络分区和合并带来的问题,提出研究与设计一种移动自组网中的“虚拟动态骨干网”模型。主要从组成员认证和组密钥管理两方面进行了研究,并基于虚拟动态骨干网提出了初步的解决方案。     

A framework for exploring organizational structure in dynamic social networks

Recent research has provided promising results relating to discovering communities within a social network. We find that further representing the organizational structure of a social network is an interesting issue that helps gain better understandings of the social network. In this paper, we define a data structure, named Community Tree, to depict the organizational structure and provide a framework for exploring the organizational structure in a social network. In this framework, an algorithm, which combines a modified PageRank and Random Walk on graph, is developed to derive the community tree from the social network. In the real world, a social network is constantly evolving. In order to explore the organizational structure in a dynamic social network, we develop a tree learning algorithm, which employs tree edit distance as the scoring function, to derive an evolving community tree that enables a smooth transition between two community trees. We also propose an approach to threading communities in community trees to obtain an evolution graph of the organizational structure, by which we can reach new insights from the dynamic social network. The experiments conducted on synthetic and real dataset demonstrate the feasibility and applicability of the framework. Based on the theoretical outcomes, we further apply the proposed framework to explore the evolution of organizational structure with the 2001 Enron dataset, and obtain several interesting findings that match the context of Enron.     

基于重叠社区和结构洞度的社会网络结构洞识别算法

结构洞是社会网络中的关键位置,对信息扩散起中介作用。为高效、准确地辨识具有社团结构的社会网络中占据结构洞的节点,提出了一种基于重叠社区和结构洞度的结构洞识别算法,旨在找到一组最具信息优势和控制优势的节点。基本思想是首先定位社区之间的重叠节点,然后利用节点的邻接差异和连接的社区差异衡量其非冗余性,计算出重叠节点的结构洞度,通过对结构洞度值升序排列发现占据结构洞的节点集。应用于实际数据集的实验结果表明,与网络约束系数算法、中介中心度算法、MaxD算法相比,该算法的识别准确度最高,时间复杂度最低。     

Revising recurrent neural networks from a granular perspective

In this paper, we formally deduce a new computational model, with a recurrent structure, by means of data granulation. The proposed scheme can be regarded as an Echo State Network (ESN), with an additional granular layer. ESNs have been recently revisited in the context of deep learning. In view of such a state-of-the-art, and coherently with the concept of data granulation, the aim herein is to propose a more efficient and transparent structure. The stability of the proposed scheme is formally discussed. The performance is shown by means of several benchmarks against the state-of-the-art methods. The proposed architecture exhibits a lower computational cost and a higher accuracy.     

Impact of a transmission facility link failure on dynamic call routing circuit-switched networks under various circuit layout policies

A major transmission facility failure such as a fiber cut can significantly deteriorate the performance of a network by affecting multiple trunk groups in the network. To improve network performance various restoration options (pre-planned or real-time) in the traffic and/or the transmission facility network can be addressed. In this paper, we address the implication of various pre-planned circuit layout policies (based on trunk group diversity) on dynamic routing circuit-switched networks in the event of a failure. Using a network based on realistic data, we give results on comparative network performance under different circuit layout policies and on the effect of a failure depending on the time of the day it occurs. We observe that in the event of a failure, a network with trunk group diversity provides load balancing in terms of pairwise blocking and avoids isolation of traffic pairs compared to a network without trunk group diversity. However, in some instances, the overall network blocking may not be lower in a network with trunk group diversity compared to a network without trunk group diversity.     

移动自组网的动态编址问题

作为实现移动自组网自动配置的一个关键问题,综述了节点地址的自动配置的最新进展,描述了设计移动自组网动态地址配置协议所面临的问题,对该问题研究开展以来所提出的各种主要方法和协议进行了详细对比、分析和分类阐述,为进一步的研究提出了新的建议。     

无线通信网络呼叫接纳控制策略研究综述

呼叫接纳控制（CAC）是无线通信网络无线资源管理中一个重要的组成部分,CAC策略依据一定的准则决定呼叫到达请求的允许接入或拒绝接入,其中策略设计的好坏直接影响着网络的性能和资源利用率。20多年来,CAC策略得到了广泛地研究,极大地改善了网络呼损率、中断率、分组延时、丢包率和吞吐率等性能。简要介绍了无线通信网络中与CAC的一些相关背景及CAC策略的功能定位与作用,重点介绍了近年来CAC研究的一些新的成果,并分析了这些成果仍然存在的一些问题,最后给出了对于CAC进一步研究的探讨。     

移动Ad Hoc网络信任管理综述

移动Ad Hoc网络是由移动节点组成的无线移动通信网络,具有动态拓扑、无线通信的特点,易受到各种安全威胁。信任管理为实体间的相互信任问题提供了决策框架,是移动Ad Hoc网络安全方案的基础。综合分析了移动Ad Hoc网络信任管理研究的最新进展。首先介绍了移动Ad Hoc网络中信任关系的特点及信任管理的分类,然后对每个类型的一些典型信任管理方案进行了分类论述和综合比较,最后指出了下一步研究中应当着重考虑的问题。     

基于改进相关性剪枝算法的BP神经网络的结构优化

BP网络中,隐层神经元的数目直接影响着整个网络的性能和效率,因而对BP网络的结构优化是一个非常重要的环节.本文对相关性剪枝算法进行了改进,采用减法聚类方法确定初始的网络结构,然后再用传统相关性剪枝算法重复优化网络.通过实验结果的分析,验证了改进的神经网络相关性剪枝算法对BP网络结构优化的有效性.     

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。     

Global core,and galaxy structure of networks

We propose a novel approach,namely local reduction of networks,to extract the global core(GC,for short)from a complex network.The algorithm is built based on the small community phenomenon of networks.The global cores found by our local reduction from some classical graphs and benchmarks convince us that the global core of a network is intuitively the supporting graph of the network,which is"similar to"the original graph,that the global core is small and essential to the global properties of the network,and that the global core,together with the small communities gives rise to a clear picture of the structure of the network,that is,the galaxy structure of networks.We implement the local reduction to extract the global cores for a series of real networks,and execute a number of experiments to analyze the roles of the global cores for various real networks.For each of the real networks,our experiments show that the found global core is small,that the global core is similar to the original network in the sense that it follows the power law degree distribution with power exponent close to that of the original network,that the global core is sensitive to errors for both cascading failure and physical attack models,in the sense that a small number of random errors in the global core may cause a major failure of the whole network,and that the global core is a good approximate solution to the r-radius center problem,leading to a galaxy structure of the network.     

面向5G车联网场景的移动任务动态卸载策略研究

由于车辆自身的高速移动性和资源有限性等特征,使得采用传统通信和计算手段的车联网场景无法满足用户日益增长的数据计算需求和体验质量需求。采用5G和边缘计算技术构建的新型车联网架构可以满足以上需求,但由于网络结构的变化,需设计适合新场景下的车辆任务通信和计算策略。针对5G车联网场景下的移动车辆任务动态卸载问题进行研究,提出了对应的动态任务分配策略和卸载调度低时延算法。车辆会根据提出的策略和算法将未完成的计算任务卸载到相应的 MEC 服务器或车辆上,并且计算结果将通过边缘服务器之间的联合通信或直接从被选择接受卸载任务的附近空闲车辆上直接返回给车主。仿真结果表明,所提出的策略和算法在优化卸载延迟方面具有良好的性能,并提高了用户体验质量。