使用SimpleHypergraphs通过超图分析、探索和可视化复杂网络.jl
Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl
摘要：
现实世界的复杂网络通常被建模为图。图的概念假设网络中的关系是二元的（例如，节点对之间的关系）;然而，这并不总是适用于许多现实生活中的场景，例如点对点通信方案，论文合著或社交网络交互。对于这样的场景，通常情况下，底层网络更好，更自然地由超图建模。超图是图的一种推广，其中一条（超）边可以连接任何数量的顶点。超图允许建模者拥有多关系（多对多）网络的完整表示;因此，它们非常适合分析和发现此类数据结构中更微妙的依赖关系。使用超图需要新的软件库，以便能够对它们执行操作，从基本算法（例如搜索或遍历网络）到计算重要的超图度量，再到包括更具挑战性的算法（例如社区发现）。在本文中，我们提出了一个新的软件库，SimpleHypergraphs.jl，编写的Julia语言和设计的超图上的高性能计算，并提出了两个新的算法来分析其属性：s-介数和修改的标签传播。我们还提出了各种方法超图可视化集成到我们的工具。为了展示如何在实践中利用该库，我们讨论了基于2019年Yelp挑战数据集和基于权力的游戏电视剧建立的协作网络的两个案例研究。结果是有希望的，他们证实了超图的能力，提供更多的洞察力比标准的基于图的方法。
英文摘要：
Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
