Large-Scale Embedding Learning in Heterogeneous Event Data

Heterogeneous events, which are defined as events connecting strongly-typed objects, are ubiquitous in the real world. We propose a HyperEdge-Based Embedding (HEBE) framework for heterogeneous event data, where a hyperedge represents the interaction among a set of involving objects in an event. The HEBE framework models the proximity among objects in an event by predicting a target object given the other participating objects in the event (hyperedge). Since each hyperedge encapsulates more information on a given event, HEBE is robust to data sparseness. In addition, HEBE is scalable when the data size spirals. Extensive experiments on large-scale real-world datasets demonstrate the efficacy and robustness of HEBE.