Search Results for author:
Found 10 papers, 5 papers with code
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well?
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM.
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
However, GNN explanation for link prediction (LP) is lacking in the literature.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
ScatterSample employs a sampling module termed DiverseUncertainty to collect instances with large uncertainty from different regions of the sample space for labeling.
TempoQR: Temporal Question Reasoning over Knowledge Graphs
The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings.
Ranked #1 on
Question Answering
on CronQuestions
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features
For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Consistency training is a popular method to improve deep learning models in computer vision and natural language processing.
Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features
Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e. g., text-based, categorical, continuous, etc.).
Relational Graph Neural Networks for Fraud Detection in a Super-App environment
Large digital platforms create environments where different types of user interactions are captured, these relationships offer a novel source of information for fraud detection problems.
