Learning Topological Representation for Networks via Hierarchical Sampling
This work addresses the limitation of existing methods in capturing global topology for network analysis, offering a domain-specific improvement for researchers and practitioners in network science.
The authors tackled the problem of network representation learning by proposing HSRL, a framework that captures both local and global topological information through hierarchical sampling, achieving improved link prediction performance on five real-world datasets.
The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages in analyzing large-scale networks. However, most existing NRL methods are designed to preserve the local topology of a network, they fail to capture the global topology. To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network. Specifically, HSRL recursively compresses an input network into a series of smaller networks using a community-awareness compressing strategy. Then, an existing NRL method is used to learn node embeddings for each compressed network. Finally, the node embeddings of the input network are obtained by concatenating the node embeddings from all compressed networks. Empirical studies for link prediction on five real-world datasets demonstrate the advantages of HSRL over state-of-the-art methods.