71 citations · 248 across the 24 of their papers we have counts for
9 papers · 1 filter
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery
Carl Yang, Mengxiong Liu, Frank He +3
Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both netw…
I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application
Carl Yang, Xiaolin Shi, Jie Luo +1
As online platforms are striving to get more users, a critical challenge is user churn, which is especially concerning for new users. In this paper, by taking the anonymous large-s…
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…