260 citations · 333 across the 15 of their papers we have counts for
6 papers · 1 filter
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
Xiang Li, Tiandi Ye, Caihua Shan +2
Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised lear…
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.…
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Di Jin, Luzhi Wang, Yizhen Zheng +4
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classifica…
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…
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
Caihua Shan, Nikos Mamoulis, Reynold Cheng +3
In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works…