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Found 10 papers, 7 papers with code
GRAND+: Scalable Graph Random Neural Networks
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
Ranked #1 on
Node Classification
on MAG-scholar-C
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks
Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise.
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #3 on
Node Property Prediction
on ogbn-papers100M
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
In this paper, we show the potential hazards of inappropriate augmentations and then propose a novel Collaborative Graph Contrastive Learning framework (CGCL).
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e. g., course concept extraction, prerequisite relation discovery, etc.
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering
This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA).
