In contrast to regular (simple) networks, hyper networks possess the ability
to depict more complex relationships among nodes and store extensive
information. Such networks are commonly found in real-world applications, such
as in social interactions. Learning embedded representations for nodes involves
a process that translates network structures into more simplified spaces,
thereby enabling the application of machine learning approaches designed for
vector data to be extended to network data. Nevertheless, there remains a need
to delve into methods for learning embedded representations that prioritize
structural aspects. This research introduces HyperS2V, a node embedding
approach that centers on the structural similarity within hyper networks.
Initially, we establish the concept of hyper-degrees to capture the structural
properties of nodes within hyper networks. Subsequently, a novel function is
formulated to measure the structural similarity between different hyper-degree
values. Lastly, we generate structural embeddings utilizing a multi-scale
random walk framework. Moreover, a series of experiments, both intrinsic and
extrinsic, are performed on both toy and real networks. The results underscore
the superior performance of HyperS2V in terms of both interpretability and
applicability to downstream tasks.

该研究介绍了 HyperS2V，一种以超级网络的结构相似性为中心的节点嵌入方法。研究通过建立超级度的概念来捕捉超级网络中节点的结构特性，并提出了一种用于衡量不同超级度之间结构相似性的新函数。最后，利用多尺度随机游走框架生成结构嵌入。通过在玩具网络和真实网络上进行的一系列内部和外部实验，结果表明 HyperS2V 在可解释性和对下游任务的适用性方面表现出优越性。