Cross-linguistic similarity is a term so broad and multi-faceted that it is not easily defined. The degree of overlap between languages is known to affect lexical competition during online processing and production, and its relevance for second language acquisition has also been established. Nevertheless, determining what makes two languages similar (or not) increases in complexity when multiple levels of the language hierarchy (e.g., phonology, syntax) are considered. How can we feasibly account for the patterns of convergence and divergence at each level of representation, as well as the interactions between them? The growing field of network science brings new methodologies to bear on this longstanding question. Below, we summarize current network science approaches to modeling language structure and discuss implications for understanding various linguistic processes. Critically, we stress the particular value of multilayer techniques, unique and powerful in their ability to simultaneously accommodate an array of node-to-node (or word-to-word) relationships.

跨语言相似性是一个如此广泛和多方面的术语，以至于不容易定义。众所周知，语言之间的重叠程度会影响在线处理和生产过程中的词汇竞争，并且它与第二语言习得的相关性也已确定。然而，当考虑语言层次结构的多个级别（例如，音系、句法）时，确定什么使两种语言相似（或不相似）会增加复杂性。我们如何切实地解释每个表示级别的收敛和发散模式，以及它们之间的相互作用？不断发展的网络科学领域为解决这个长期存在的问题带来了新的方法论。以下，我们总结了当前用于建模语言结构的网络科学方法，并讨论了对理解各种语言过程的影响。至关重要的是，我们强调多层技术的特殊价值，它们独特而强大，能够同时容纳一系列节点到节点（或词到词）关系。