Locality and Attachedness-based Temporal Social Network Growth Dynamics Analysis A case study on evolving nanotechnology scientific networks
Locality and Attachedness-based Temporal Social Network Growth Dynamics Analysis A case study on evolving nanotechnology scientific networks
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基于局部性和依附性的时间社会网络增长动态分析关于不断发展的纳米技术科学网络的案例研究
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发表时间：
2009
期刊：
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通讯作者：
J. Yen
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作者：
Haizheng Zhang;Baojun Qiu;K. Ivanova;C. Lee Giles;Henry C. Foley;J. Yen
在过去的十年中，纳米技术研究和开发的快速发展为科学计量学研究提供了一个极好的机会，因为它可以提供与这个令人兴奋的领域相关的快速发展的社交网络的动态增长的见解。在本文中，我们进行了纳米技术的案例研究，以发现的动力，管理的快速发展的科学合作网络的增长过程。本文从时间社交网络的定义开始，并证明了纳米技术合作网络，在类似于其他现实世界的社交网络，表现出一组有趣的静态和动态拓扑特性。在协作网络中，新的连接往往会增加邻近节点之间的观察的启发，我们探讨了不断增长的网络中的局部性因素和附着性因素。特别地，2 Haizheng Zhang等人开发了两种基于距离的计算网络增长方案，即DG和DDG。DG模型只考虑局部性元素，而DDG是一个混合模型，考虑到局部性和附着性元素。仿真结果表明，聚类系数率和平均最短距离与边缘致密化率密切相关。此外，混合DDG模型表现出较高的聚类系数值和降低平均最短距离时，边缘致密化率是固定的，这意味着结合本地性和附着性可以更好地表征纳米技术社区的成长过程。基于模拟结果，我们得出结论，社会网络的演化与依恋和局部性因素。
The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights on the dynamic growth of the fast evolving social networks associated with this exciting field. In this paper we conduct a case study on nanotechnology in order to discover the dynamics that govern the growth process of rapidly advancing scientific collaboration networks. This paper starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, in resemblance of other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality factor and the attachedness factor in growing networks. In particular, 2 Haizheng Zhang et al. we develop two distance-based computational network growth schemes, namely DG and DDG. The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results of these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results we conclude that social network evolution is related to both attachedness and locality factors.