论文标题
从立方体到网络：合成网络生成的快速通用模型
From Cubes to Networks: Fast Generic Model for Synthetic Networks Generation
论文作者
论文摘要
对复杂网络和立方体（即多维数据集）的分析探索目前是两个具有不同策略的独立研究领域。为了通过唯一的网络域方法获得更多对立方体动力学的见解并获得丰富的合成网络，我们需要从立方体到相关网络的转换方法。为此，我们提出了FGM，这是一种快速通用模型，将立方体转换为相互关联的网络，从而将样品重塑为节点，并且网络动态受到最近邻居搜索的概念的指导。通过与以前的模型进行比较，我们表明，FGM可以成本效率地生成表现出与事实网络更紧密相符的典型模式的网络，例如更真实的程度分布，幂律的平均近端近端程度依赖性以及我们认为对网络至关重要的影响力衰减现象。此外，我们评估了FGM通过各种立方体生成的网络。结果表明，FGM对输入扰动具有弹性，从而产生具有一致性属性的网络。
Analytical explorations on complex networks and cubes (i.e., multi-dimensional datasets) are currently two separate research fields with different strategies. To gain more insights into cube dynamics via unique network-domain methodologies and to obtain abundant synthetic networks, we need a transformation approach from cubes into associated networks. To this end, we propose FGM, a fast generic model converting cubes into interrelated networks, whereby samples are remodeled into nodes and network dynamics are guided under the concept of nearest-neighbor searching. Through comparison with previous models, we show that FGM can cost-efficiently generate networks exhibiting typical patterns more closely aligned to factual networks, such as more authentic degree distribution, power-law average nearest-neighbor degree dependency, and the influence decay phenomenon we consider vital for networks. Furthermore, we evaluate the networks that FGM generates through various cubes. Results show that FGM is resilient to input perturbations, producing networks with consistent fine properties.
