140 citations · 246 across the 7 of their papers we have counts for
5 papers · 1 filter
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
Yu Shi, Xinwei He, Naijing Zhang +2
Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic…
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Yu Shi, Qi Zhu, Fang Guo +2
Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from netw…
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks
Yu Shi, Huan Gui, Qi Zhu +2
Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order…
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
Yu Shi, Fangqiu Han, Xinwei He +4
Multi-view networks are broadly present in real-world applications. In the meantime, network embedding has emerged as an effective representation learning approach for networked da…
PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks
Yu Shi, Po-Wei Chan, Honglei Zhuang +2
As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures h…