There is very little work on using graph embedding for semantic proximity search. We also observe that graph embedding methods typically focus on embedding.
Feb 10, 2017 · As the core of semantic proximity search, we have to measure the proximity on a heterogeneous graph, whose nodes are various types of objects.
This work introduces a new concept of proximity embedding, which directly embeds the network structure between two possibly distant nodes and can easily ...
Thus, we introduce a new concept of proximity embedding, which directly embeds the network structure between two possibly distant nodes. We also design our ...
ABSTRACT. Semantic proximity search on heterogeneous graph is an impor- tant task, and is useful for many applications. It aims to measure.
Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes ...
Semantic Proximity Search on Heterogeneous Graph by Proximity ...
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Thus, we introduce a new concept of proximity embedding, which directly embeds the network structure between two possibly distant nodes. We also design our ...
We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.
The "java - prepare data for model" directory is used to prepare input data for ProxEmbed model, while "python - model" directory is used to generate and assess ...
Proximity search on heterogeneous graphs aims to measure the proximity between two nodes on a graph w.r.t. some semantic relation for ranking. Pioneer work ...