2019/06/05 by Li‐Qun Chen, Chen, Liqun, Guoyin Wang +15 · 1 citation
Computer Science · Psychology · #Advanced Graph Neural Networks #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mental Health via Writing #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1906.01840
openalex publication_date 2019/06/05 · openalex created_date 2019/06/14 · openalex updated_date 2026/07/28
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network-embedding problem, and present two novel strategies to improve over traditional attention mechanisms: (i) a content-aware sparse attention module based on optimal transport, and (ii) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.