15 citations · 25 across the 5 of their papers we have counts for
17 papers
Contrastive Graph Few-Shot Learning
Chunhui Zhang, Hongfu Liu, Jundong Li +2
Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradati…
Automated Generation of Interorganizational Disaster Response Networks through Information Extraction
Yitong Li, Duoduo Liao, Jundong Li +1
When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders. Aiming at reducing the efforts associated w…
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Kaize Ding, Jianling Wang, Jundong Li +2
Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention i…
Line Graph Neural Networks for Link Prediction
Lei Cai, Jundong Li, Jie Wang +1
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link predicti…
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Kaize Ding, Jianling Wang, Jundong Li +3
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central an…
Scalable Attack on Graph Data by Injecting Vicious Nodes
Jihong Wang, Minnan Luo, Fnu Suya +3
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph…