Abstract
Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should be pushed together and away in the embedding space, nor model coarse-grained community knowledge hidden behind the network topology. In this paper, we propose a novel NRL framework, Structural Hierarchy Enhancement (SHE), to deal with such two issues. The main idea is to construct a structural hierarchy from the network based on community detection, and to utilize such a hierarchy to perform level-wise NRL. In addition, lower-level node embeddings are passed to higher-level ones so that community knowledge can be aware of in NRL. Experiments conducted on benchmark network datasets show that SHE can significantly boost the performance of NRL in both tasks of NC and LP, compared to other hierarchical NRL methods.
Highlights
Network representation learning (NRL) is a crucial task in social and information network analysis
The improvement margin is around 60% and 30% for node classification (NC) and link prediction (LP), respectively
This paper proposes a structural hierarchy-enhanced network representation learning (SHE-NRL)
Summary
Network representation learning (NRL) is a crucial task in social and information network analysis. The idea of NRL is to learn a mapping function that converts each node into a low-dimensional embedding space while preserving the structural proximity between nodes in the given network. The derived node embedding vectors can be utilized for downstream tasks, including node classification, link prediction, and community detection. Metapath2vec [4] extends the skip-gram based NRL to heterogeneous information networks that contain multiple types of nodes and links. DANE [5] incorporates the similarity between node attributes into NRL. GCN [6] further learns node embeddings for semi-supervised node classification through a layer-wise propagation with graph convolution
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