Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
This addresses the need for application-specific graph embeddings in domains like knowledge graphs and social networks, though it is incremental as it builds on existing embedding methods.
The paper tackles the problem of graph embedding methods being disconnected from target applications by proposing Customized Graph Embedding (CGE), which learns tailored node vectors by differentiating path importance for specific applications, and demonstrates strong performance on node classification datasets.
Graph is a natural representation of data for a variety of real-word applications, such as knowledge graph mining, social network analysis and biological network comparison. For these applications, graph embedding is crucial as it provides vector representations of the graph. One limitation of existing graph embedding methods is that their embedding optimization procedures are disconnected from the target application. In this paper, we propose a novel approach, namely Customized Graph Embedding (CGE) to tackle this problem. The CGE algorithm learns customized vector representations of graph nodes by differentiating the importance of distinct graph paths automatically for a specific application. Extensive experiments were carried out on a diverse set of node classification datasets, which demonstrate strong performances of CGE and provide deep insights into the model.