利用局部信息进行学位排序
Degree Ranking Using Local Information
摘要：
大多数真实世界的动态网络都随着时间的推移而快速发展。在任何给定时间收集整个网络来研究其特性是不可行的。这就需要提出局部算法来研究网络的各种属性。在目前的工作中，我们估计度排名的一个节点，而没有整个网络。所提出的方法是基于幂律度分布特性或采样技术。所提出的方法模拟合成网络，以及在现实世界的社交网络。所提出的方法的效率进行评估，使用绝对和加权误差函数。结果表明，仅使用网络规模的$1\%$样本就可以高精度地估计节点的度秩。估计的准确性从高排名到低排名节点降低。我们进一步扩展了随机网络的方法，并验证了其效率的合成随机网络，使用Erdens-Rényi模型生成。结果表明，所提出的方法可以有效地用于随机网络以及。
英文摘要：
Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only $1\%$ samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for random networks and validate their efficiency on synthetic random networks, that are generated using Erdős-Rényi model. Results show that the proposed methods can be efficiently used for random networks as well.
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