87 citations · 122 across the 13 of their papers we have counts for
6 papers · 1 filter
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
Mingxuan Ju, Yujie Fan, Chuxu Zhang +1
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security sta…
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju +2
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most exist…
Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
Mingxuan Ju, Wenhao Yu, Tong Zhao +2
A common thread of open-domain question answering (QA) models employs a retriever-reader pipeline that first retrieves a handful of relevant passages from Wikipedia and then peruse…
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen +4
Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capabilit…
Generate rather than Retrieve: Large Language Models are Strong Context Generators
Wenhao Yu, Dan Iter, Shuohang Wang +6
Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tas…
Black-box Node Injection Attack for Graph Neural Networks
Mingxuan Ju, Yujie Fan, Yanfang Ye +1
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommend…