38 citations · 48 across the 7 of their papers we have counts for
7 papers
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng +4
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily.…
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Jingwei Zhuo, Bin Liu, Xiang Li +2
Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates l…
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
Gang Wang, Ziyi Guo, Xiang Li +2
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference. A key component in collaborative filtering is representation learnin…
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
Xiang Li, Danhao Ding, Ben Kao +2
A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types. We study the probl…
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data
Xiang Li, Ben Kao, Caihua Shan +2
We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities. Traditional spectral clustering t…
A General Early-Stopping Module for Crowdsourced Ranking
Caihua Shan, Leong Hou U, Nikos Mamoulis +2
Crowdsourcing can be used to determine a total order for an object set (e.g., the top-10 NBA players) based on crowd opinions. This ranking problem is often decomposed into a set o…