复杂网络集团特征研究综述

自然界和人类社会的许多系统可以用复杂网络进行建模，复杂网络已成为多个学科的研究热点。分析复杂网络的一个关键问题是如何理解其全局组织，网络的健壮性和稳定性在很大程度上取决于其集团结构特征。本文简要介绍了复杂网络的基本概念并详细总结了近年复杂网络集团特性的研究进展，重点分析了社区发现算法的最新研究成果，最后提出这一领域几个有待解决的问题和可能的发展方向。     

异质信息网络表征学习综述

随着信息技术的快速发展,信息网络无处不在,例如社交网络、学术网络、万维网等.由于网络规模不断扩大以及数据的稀疏性,信息网络的分析方法面临巨大挑战.作为应对网络规模及数据稀疏挑战的有效方法,信息网络表征学习旨在利用网络的拓扑结构、节点内容等信息将节点嵌入到低维的向量空间中,同时保留原始网络固有的结构特征和内容特征,从而使节点的分类、聚类、链路预测等网络分析任务能够基于低维、稠密的向量完成.由多种类型的节点和连边构成的异质信息网络包含更加全面、丰富的结构和语义信息,因此异质信息网络的表征学习不仅能够有效缓解网络数据高维、稀疏性问题,还能融合网络中不同类型的异质信息,使学习到的表征更有意义和价值.近年来,异质信息网络的表征学习受到学术界和工业界的广泛关注,成为网络分析的一个重要研究主题,研究成果不断涌现.然而,目前还缺乏对现有成果进行全面梳理的工作,相关研究人员难以系统地了解最新研究进展,在实际应用中也难以选择合适的嵌入模型.为此,本文对异质信息网络表征学习的方法进行了全面综述,包括相关概念、网络分类、学习方法、数据集与测评指标、典型应用,同时对未来的研究方向进行了展望.本文工作有助于研究人员全面系统地了解异质信息网络表征学习的研究进展,也有助于从业人员更有效地解决实际应用问题.     

复杂网络中近似最短路径问题

随着网络规模的不断增大,经典算法(如Dijkstra等)效率越来越低.针对这一问题,研究者们提出了许多近似搜索算法,但如何既能提高搜索效率又能保持准确性一直是一大难点.本文根据复杂网络的结构特性引入区域划分,同时改进树分解的构造,将图构造成一棵树进行搜索,得到了一个新的适合于复杂网络的最短路径近似算法.此外通过实例验证,该算法不仅在一定程度上降低了计算复杂性,而且保持了较高的近似准确性.     

A Network of Neural Oscillators for Fractal Pattern Recognition

Biological neural networks are high dimensional nonlinear systems, which presents complex dynamical phenomena, such as chaos. Thus, the study of coupled dynamical systems is important for understanding functional mechanism of real neural networks and it is also important for modeling more realistic artificial neural networks. In this direction, the study of a ring of phase oscillators has been proved to be useful for pattern recognition. Such an approach has at least three nontrivial advantages over the traditional dynamical neural networks: first, each input pattern can be encoded in a vector instead of a matrix; second, the connection weights can be determined analytically; third, due to its dynamical nature, it has the ability to capture temporal patterns. In the previous studies of this topic, all patterns were encoded as stable periodic solutions of the oscillator network. In this paper, we continue to explore the oscillator ring for pattern recognition. Specifically, we propose algorithms, which use the chaotic dynamics of the closed loops of Stuart–Landau oscillators as artificial neurons, to recognize randomly generated fractal patterns. We manipulate the number of neurons and initial conditions of the oscillator ring to encode fractal patterns. It is worth noting that fractal pattern recognition is a challenging problem due to their discontinuity nature and their complex forms. Computer simulations confirm good performance of the proposed algorithms.     

异质信息网络的复杂条件社区搜索

社区是信息网络的重要属性, 社区搜索旨在寻找满足用户给定条件的节点集合, 是信息网络分析的重要研究内容. 异质信息网络由于包含更加全面、丰富的结构和语义信息, 所以异质信息网络的社区搜索近年来受到人们的广泛关注. 针对现有异质信息网络的社区搜索方法难以满足复杂条件社区搜索要求的不足, 定义了复杂条件社区搜索问题, 提出了考虑非对称元路径、受限元路径和禁止节点约束的搜索算法. 3种算法分别通过元路径补全策略、调整带标签的批量搜索策略和拆分复杂搜索条件的方式搜索社区, 同时针对禁止节点约束的搜索算法设计了基于剪枝策略和近似策略的优化算法以提高搜索效率. 在真实数据集上进行了大量实验, 实验结果证明了所提算法的有效性和高效性.     

Detecting community structure in complex networks using genetic algorithm based on object migrating automata

Community structure is an important topological feature of complex networks. Detecting community structure is a highly challenging problem in analyzing complex networks and has great importance in understanding the function and organization of networks. Up until now, numerous algorithms have been proposed for detecting community structure in complex networks. A wide range of these algorithms use the maximization of a quality function called modularity. In this article, three different algorithms, namely, MEM-net, OMA-net, and GAOMA-net, have been proposed for detecting community structure in complex networks. In GAOMA-net algorithm, which is the main proposed algorithm of this article, the combination of genetic algorithm (GA) and object migrating automata (OMA) has been used. In GAOMA-net algorithm, the MEM-net algorithm has been used as a heuristic to generate a portion of the initial population. The experiments on both real-world and synthetic benchmark networks indicate that GAOMA-net algorithm is efficient for detecting community structure in complex networks.     

Clustering overhead and convergence time analysis of the mobility-based multi-hop clustering algorithm for mobile ad hoc networks

With the emergence of large mobile ad hoc networks, the ability of existing routing protocols to scale well and function satisfactorily comes into question. Clustering has been proposed as a means to divide large networks into groups of suitably smaller sizes such that prevailing MANET routing protocols can be applied. However, the benefits of clustering come at a cost. Clusters take time to form and the clustering algorithms also introduce additional control messages that contend with data traffic for the wireless bandwidth. In this paper, we aim to analyse a distributed multi-hop clustering algorithm, Mobility-based D-Hop (MobDHop), based on two key clustering performance metrics and compare it with other popular clustering algorithms used in MANETs. We show that the overhead incurred by multi-hop clustering has a similar asymptotic bound as 1-hop clustering while being able to reap the benefits of multi-hop clusters. Simulation results are presented to verify our analysis.     

互信息与多条元路径融合的异质网络表示学习方法

异质信息网络能够对真实世界的诸多复杂应用场景进行建模,其表示学习研究也得到了众多学者的广泛关注.现有的异质网络表示学习方法大多基于元路径来捕获网络中的结构和语义信息,已经在后续的网络分析任务中取得很好的效果.然而,此类方法忽略了元路径的内部节点信息和不同元路径实例的重要性;仅能捕捉到节点的局部信息.因此,提出互信息与多条元路径融合的异质网络表示学习方法.首先,利用一种称为关系旋转编码的元路径内部编码方式,基于相邻节点和元路径上下文节点捕获异质信息网络的结构和语义信息,采用注意力机制来建模各元路径实例的重要性;然后,提出一种互信息最大化与多条元路径融合的无监督异质网络表示学习方法,使用互信息捕获全局信息以及全局信息和局部信息之间的联系.最后,在两个真实数据集上进行实验,并与当前主流的算法进行比较分析.结果表明,所提方法在节点分类和聚类任务上性能都有提升,甚至和一些半监督算法相比也表现出强劲性能.     

Editorial

Wireless sensor network(WSN)is characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon.The main advantages of WSN include its low cost,rapid deployment,self-organization,and fault tolerance.WSN has received tremendous interests of various research communities,and significant progresses have been made in various aspects including sensor platform development,wireless communication and networking,signal and information processing,as well as network performance eva...     

A framework for simulating and estimating the state and functional topology of complex dynamic geometric networks

We introduce a framework for simulating signal propagation in geometric networks (networks that can be mapped to geometric graphs in some space) and developing algorithms that estimate (i.e., map) the state and functional topology of complex dynamic geometric networks. Within the framework, we define the key features typically present in such networks and of particular relevance to biological cellular neural networks: dynamics, signaling, observation, and control. The framework is particularly well suited for estimating functional connectivity in cellular neural networks from experimentally observable data and has been implemented using graphics processing unit high-performance computing. Computationally, the framework can simulate cellular network signaling close to or faster than real time. We further propose a standard test set of networks to measure performance and compare different mapping algorithms.