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
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-
B
ased

E
mbedding
(
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.