The Role of Graphlets in Viral Processes on Networks
The Role of Graphlets in Viral Processes on Networks
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DOI：
10.1007/s00332-018-9465-y
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发表时间：
2018-05
影响因子：
3
通讯作者：
Samira Khorshidi;Mohammad Al Hasan;G. Mohler;M. Short
中科院分区：
文献类型：
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作者：
Samira Khorshidi;Mohammad Al Hasan;G. Mohler;M. Short
预测网络上病毒过程的演变是生物学、社会科学和互联网研究中出现的应用的一个重要问题。在现有的工作中，基于度分布的平均场分析用于预测病毒在不同类型网络中的传播。然而，事实证明，仅靠度分布无法预测病毒在某些现实世界网络上的行为，并且最近已尝试使用相配性来解决这一缺点。在本文中，我们表明添加相配性并不能完全解释许多现实世界网络中病毒传播的差异。我们建议使用图基数频率分布与相配性相结合来解释具有相同程度分布的网络中病毒过程进化的变化。使用数据驱动的方法，将预测模型与现实世界网络上的病毒式传播过程模拟相结合，我们证明基于图基数频率分布的简单回归模型可以解释具有相同程度分布但不同网络拓扑的网络上超过 95% 的病毒式传播方差。我们的结果不仅强调了 graphlet 的重要性，而且还确定了一小部分 graphlet 的集合，这些 graphlet 可能对网络上的病毒传播过程具有最大的影响。
Predicting the evolution of viral processes on networks is an important problem with applications arising in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used for the prediction of viral spreading across networks of different types. However, it has been shown that degree distribution alone fails to predict the behavior of viruses on some real-world networks and recent attempts have been made to use assortativity to address this shortcoming. In this paper, we show that adding assortativity does not fully explain the variance in the spread of viruses for a number of real-world networks. We propose using the graphlet frequency distribution in combination with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution. Using a data-driven approach by coupling predictive modeling with viral process simulation on real-world networks, we show that simple regression models based on graphlet frequency distribution can explain over 95% of the variance in virality on networks with the same degree distribution but different network topologies. Our results not only highlight the importance of graphlets but also identify a small collection of graphlets which may have the highest influence over the viral processes on a network.