140 citations · 246 across the 7 of their papers we have counts for
7 papers
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
Carl Yang, Yichen Feng, Pan Li +2
In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner.…
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Yu Shi, Jiaming Shen, Yuchen Li +7
Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the…
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…