LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles.
Sign Up to like articles & get recommendations!
Embedding Learning with Events in Heterogeneous Information Networks
In real-world applications, objects of multiple types are interconnected, forming Heterogeneous Information Networks. In such heterogeneous information networks, we make the key observation that many interactions happen due to some… Click to show full abstract
In real-world applications, objects of multiple types are interconnected, forming Heterogeneous Information Networks. In such heterogeneous information networks, we make the key observation that many interactions happen due to some event and the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework called HyperEdge- BasedEmbedding (Hebe) to learn object embeddings with events in heterogeneous information networks, where a hyperedge encompasses the objects participating in one event. The Hebe framework models the proximity among objects in each event with two methods: (1) predicting a target object given other participating objects in the event, and (2) predicting if the event can be observed given all the participating objects. Since each hyperedge encapsulates more information of a given event, Hebe is robust to data sparseness and noise. In addition, Hebe is scalable when the data size spirals. Extensive experiments on large-scale real-world datasets show the efficacy and robustness of the proposed framework.
Share on Social Media:
  
        
        
        
Sign Up to like & get recommendations! 0
Related content
More Information
            
News
            
Social Media
            
Video
            
Recommended
               
Click one of the above tabs to view related content.