2024/01/01 by Meng Qin, Qin, Meng, Dit‐Yan Yeung +1 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Artificial intelligence #Bayesian Modeling and Causal Inference #Combinatorics #Complex Network Analysis Techniques #Computer science #Embedding #FOS: Computer and information sciences #Graph #Graph embedding #Identity (music) #Inference #Line graph #Mathematics #Node (physics) #Position (finance) #Property (philosophy) #Random walk #Social and Information Networks (cs.SI) #Theoretical computer science #Topological graph theory #Topology (electrical circuits) #Voltage graph
paper · pdf · doi:10.48550/arxiv.2401.00651
openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Network embedding, which maps graphs to distributed representations, is a unified framework for various graph inference tasks. According to the topology properties (e.g., structural roles and community memberships of nodes) to be preserved, it can be categorized into the identity and position embedding. Most existing methods can only capture one type of property. Some approaches can support the inductive inference that generalizes the embedding model to new nodes or graphs but relies on the availability of attributes. Due to the complicated correlations between topology and attributes, it is unclear for some inductive methods which type of property they can capture. In this study, we explore a unified framework for the joint inductive inference of identity and position embeddings without attributes. An inductive random walk embedding (IRWE) method is proposed, which combines multiple attention units to handle the random walk (RW) on graph topology and simultaneously derives identity and position embeddings that are jointly optimized. We demonstrate that some RW statistics can characterize node identities and positions while supporting the inductive inference. Experiments validate the superior performance of IRWE over various baselines for the transductive and inductive inference of identity and position embeddings.