Adaptive Graph Embedding with Relational Context

Abstract

Graph embedding techniques have become increasingly prevalent in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, traditional graph embedding methods often treat nodes and edges independently, neglecting the crucial relational context that governs the structure of the graph. This paper introduces an adaptive graph embedding method designed to address this limitation. The core idea is to learn embeddings by explicitly incorporating the relationships between nodes, leveraging the contextual information surrounding each connection. We employ a recurrent neural network (RNN) to process this relational context, generating embeddings that are sensitive to these contextual dependencies. The resulting embeddings are expected to provide more accurate and informative representations of nodes compared to methods that disregard relational context. This work demonstrates the potential of incorporating contextual information into graph embedding, leading to improved performance across a range of graph-based tasks.