CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous Information Network

arXiv:2405.08013v1 Announce Type: new
Abstract: Inductive representation learning on temporal heterogeneous graphs is crucial for scalable deep learning on heterogeneous information networks (HINs) which are time-varying, such as citation networks. However, most existing approaches are not inductive and thus cannot handle new nodes or edges. Moreover, previous temporal graph embedding methods are often trained with the temporal link prediction task to simulate the link formation process of temporal graphs, while ignoring the evolution of high-order topological structures on temporal graphs. …

abstract arxiv continuous cs.ai cs.lg cs.si deep learning embedding graph graphs however inductive information network networks nodes representation representation learning scalable temporal type