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06/05/2023
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Model pre-training on large text corpora has been demonstrated effective...
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06/01/2023
SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated promising outcomes across...
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04/20/2023
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
How can we learn effective node representations on textual graphs? Graph...
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09/30/2022
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring importa...
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09/13/2022
Variational Causal Inference
Estimating an individual's potential outcomes under counterfactual treat...
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06/22/2022
Efficient and effective training of language and graph neural network models
Can we combine heterogenous graph structure with text to learn high-qual...
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06/16/2022
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features
Graph Neural Networks (GNNs) with numerical node features and graph stru...
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12/10/2021
TempoQR: Temporal Question Reasoning over Knowledge Graphs
Knowledge Graph Question Answering (KGQA) involves retrieving facts from...
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10/26/2021
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features
For supervised learning with tabular data, decision tree ensembles produ...
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10/12/2021
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Consistency training is a popular method to improve deep learning models...
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04/17/2021
Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks
Uncovering anomalies in attributed networks has recently gained populari...
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07/24/2020
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
The coronavirus disease (COVID-19) has claimed the lives of over 350,000...
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07/20/2020
PanRep: Universal node embeddings for heterogeneous graphs
Learning unsupervised node embeddings facilitates several downstream tas...
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07/20/2020
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
Predicting interactions among heterogenous graph structured data has num...
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03/15/2020
Tensor Graph Convolutional Networks for Multi-relational and Robust Learning
The era of "data deluge" has sparked renewed interest in graph-based lea...
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01/27/2020
Efficient and Stable Graph Scattering Transforms via Pruning
Graph convolutional networks (GCNs) have well-documented performance in ...
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10/21/2019
Edge Dithering for Robust Adaptive Graph Convolutional Networks
Graph convolutional networks (GCNs) are vulnerable to perturbations of t...
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10/21/2019
GraphSAC: Detecting anomalies in large-scale graphs
A graph-based sampling and consensus (GraphSAC) approach is introduced t...
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11/05/2018
A Recurrent Graph Neural Network for Multi-Relational Data
The era of data deluge has sparked the interest in graph-based learning ...
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09/22/2018
Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
Joint analysis of data from multiple information repositories facilitate...
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05/16/2018
Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
Network science provides valuable insights across numerous disciplines i...
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11/28/2017
Kernel-based Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past de...
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11/25/2017
