HyperS2V: A Framework for Structural Representation of Nodes in Hyper Networks
This work addresses the need for structural representation methods in hyper networks, which are common in real-world applications like social interactions, but it appears incremental as it builds on existing embedding techniques.
The paper tackles the problem of learning node embeddings that prioritize structural aspects in hyper networks, introducing HyperS2V, which uses hyper-degrees and a multi-scale random walk framework to achieve superior performance in interpretability and applicability to downstream tasks.
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.