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.

翻译：对复杂网络与多维数据立方体的分析探索目前是两个独立的研究领域，采用不同的策略。为了通过独特的网络域方法深入理解数据立方体动态特性，并获取丰富的合成网络，我们需要一种将数据立方体转化为关联网络的转换方法。为此，我们提出FGM——一种将数据立方体快速转换为相互关联网络的通用模型，其中样本被重塑为节点，网络动态在最近邻搜索概念指导下进行。通过与先前模型的对比，我们证明FGM能够以高成本效益生成具有更贴近真实网络典型模式的网络，例如更真实的度分布、幂律平均最近邻度依赖性，以及我们认为对网络至关重要的影响衰减现象。此外，我们通过多种数据立方体评估了FGM生成的网络。结果表明，FGM对输入扰动具有鲁棒性，能够生成具有一致精细特性的网络。