在统计验证超图中检测信息高阶相互作用
Detecting informative higher-order interactions in statistically validated hypergraphs
摘要：
最近的经验证据表明，在许多现实世界的系统中，成功地表示为网络，交互不仅限于二对，但往往涉及三个或更多的代理在同一时间。超图可以更好地描述这些数据，其中超链接编码一组节点之间的高阶交互。尽管有大量的工作在网络上，突出信息超链接从现实世界的数据获得的超图仍然是一个悬而未决的问题。在这里，我们提出了一种分析方法来过滤超图，通过识别那些超链接，相对于一个随机的零假设过度表达，并代表最相关的高阶连接。我们将我们的方法应用到一类合成基准和几个数据集。对于所有的情况下，该方法突出显示的超链接，是更多的信息比那些提取成对的方法。我们的方法提供了第一种方法来获得统计验证的超图，分离信息连接冗余和嘈杂的。
英文摘要：
Recent empirical evidence has shown that in many real-world systems, successfully represented as networks, interactions are not limited to dyads, but often involve three or more agents at a time. These data are better described by hypergraphs, where hyperlinks encode higher-order interactions among a group of nodes. In spite of the large number of works on networks, highlighting informative hyperlinks in hypergraphs obtained from real world data is still an open problem. Here we propose an analytic approach to filter hypergraphs by identifying those hyperlinks that are over-expressed with respect to a random null hypothesis, and represent the most relevant higher-order connections. We apply our method to a class of synthetic benchmarks and to several datasets. For all cases, the method highlights hyperlinks that are more informative than those extracted with pairwise approaches. Our method provides a first way to obtain statistically validated hypergraphs, separating informative connections from redundant and noisy ones.
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。
