超图
计算机科学
嵌入
理论计算机科学
代表(政治)
灵活性(工程)
领域(数学)
人工智能
分类学(生物学)
数据科学
人工神经网络
特征学习
机器学习
数学
政治
纯数学
法学
离散数学
统计
生物
植物
政治学
作者
Alessia Antelmi,Gennaro Cordasco,Mirko Polato,Vittorio Scarano,Carmine Spagnuolo,Dingqi Yang
摘要
Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects—most commonly nodes—of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.
科研通智能强力驱动
Strongly Powered by AbleSci AI