58 citations · 60 across the 5 of their papers we have counts for
5 papers
When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction
Tianpeng Li, Xuan Guo, Wenjun Wang +2
Generative models of temporal graphs are trained on one stretch of an evolving network and deployed on the next, and they degrade badly in the gap. We show this degradation is deri…
Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
Xuan Guo, Pengfei Jiao, Ting Pan +4
Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, ex…
ANOMALYMAXQ:Anomaly-Structured Maximization to Query in Attributed Network
Xinyue Zhang, Nannan Wu, Zixu Zhen +1
The detection of anomaly subgraphs naturally appears in various real-life tasks, yet label noise seriously interferes with the result. As a motivation for our work, we focus on ina…
Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors
Xianchen Wang, Hongtao Liu, Peiyi Wang +4
Text reviews can provide rich useful semantic information for modeling users and items, which can benefit rating prediction in recommendation. Different words and reviews may have…
NRPA: Neural Recommendation with Personalized Attention
Hongtao Liu, Fangzhao Wu, Wenjun Wang +4
Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, differe…