论文标题
现实世界中超图的演变：没有甲骨文的模式和模型
Evolution of Real-world Hypergraphs: Patterns and Models without Oracles
论文作者
论文摘要
在现实世界中，我们可以观察到什么样的宏观结构和动力学模式？在显然是随机进化的超出随机进化之外，最终导致观察到的模式的个体可以实现局部动态的基本动态？ 图形提供了代表实体之间成对相互作用的有效方法，无法代表组相互作用（例如，三个或更多研究人员的协作等）。被视为图形的概括，超图允许各种边缘在解决这一限制方面富有成果。然而，增加的复杂性使得像图一样彻底了解超图像一样具有挑战性。 在这项工作中，我们仔细检查了来自六个域的实际超图的七个结构和动力学特性。为此，我们定义了新的措施，将公共图形特性的概念扩展到超图，并通过与无效模型和统计检验进行比较评估观察到的模式的重要性。 我们还提出了\ textsc {hyperff}，这是一个随机模型，用于生成逼真的超图。 Its merits are three-fold: (a) \underline{Realistic:} it successfully reproduces all seven patterns, in addition to five patterns established in previous studies, (b) \underline{Self-contained:} unlike previously proposed models, it does not rely on oracles (i.e., unexplainable external information) at all, and it is parameterized by just two scalars, and (c) \下划线{Emergent：}它依赖于对个体实体的简单和可解释的机制，这些机制并不是微不足道的，但令人惊讶地导致了宏观的特性。
What kind of macroscopic structural and dynamical patterns can we observe in real-world hypergraphs? What can be underlying local dynamics on individuals, which ultimately lead to the observed patterns, beyond apparently random evolution? Graphs, which provide effective ways to represent pairwise interactions among entities, fail to represent group interactions (e.g., collaboration of three or more researchers, etc.). Regarded as a generalization of graphs, hypergraphs allowing for various sizes of edges prove fruitful in addressing this limitation. The increased complexity, however, makes it challenging to understand hypergraphs as thoroughly as graphs. In this work, we closely examine seven structural and dynamical properties of real hypergraphs from six domains. To this end, we define new measures, extend notions of common graph properties to hypergraphs, and assess the significance of observed patterns by comparison with a null model and statistical tests. We also propose \textsc{HyperFF}, a stochastic model for generating realistic hypergraphs. Its merits are three-fold: (a) \underline{Realistic:} it successfully reproduces all seven patterns, in addition to five patterns established in previous studies, (b) \underline{Self-contained:} unlike previously proposed models, it does not rely on oracles (i.e., unexplainable external information) at all, and it is parameterized by just two scalars, and (c) \underline{Emergent:} it relies on simple and interpretable mechanisms on individual entities, which do not trivially enforce but surprisingly lead to macroscopic properties.
