research
∙
06/01/2023
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?
Graph neural networks (GNNs) have shown promising results on real-life d...
share
research
∙
04/20/2023
Multi-label Node Classification On Graph-Structured Data
Graph Neural Networks (GNNs) have shown state-of-the-art improvements in...
research
∙
07/22/2022
Privacy and Transparency in Graph Machine Learning: A Unified Perspective
Graph Machine Learning (GraphML), whereby classical machine learning is ...
research
∙
06/29/2022
Private Graph Extraction via Feature Explanations
Privacy and interpretability are two of the important ingredients for ac...
research
∙
06/28/2022
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
The problem of interpreting the decisions of machine learning is a well-...
research
∙
11/26/2021
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions
Viral infections are causing significant morbidity and mortality worldwi...
research
∙
10/12/2021
Fast Forward Indexes for Efficient Document Ranking
Neural approaches, specifically transformer models, for ranking document...
research
∙
09/27/2021
Knowledge-Aware Neural Networks for Medical Forum Question Classification
Online medical forums have become a predominant platform for answering h...
research
∙
09/18/2021
Releasing Graph Neural Networks with Differential Privacy Guarantees
With the increasing popularity of Graph Neural Networks (GNNs) in severa...
research
∙
08/08/2021
MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction
Growing evidence from recent studies implies that microRNA or miRNA coul...
research
∙
06/23/2021
Learnt Sparsification for Interpretable Graph Neural Networks
Graph neural networks (GNNs) have achieved great success on various task...
research
∙
05/18/2021
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
With the ever-increasing popularity and applications of graph neural net...
research
∙
04/16/2021
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning
When applying outlier detection in settings where data is sensitive, mec...
research
∙
04/13/2021
A Review of Anonymization for Healthcare Data
Mining health data can lead to faster medical decisions, improvement in ...
research
∙
01/18/2021
Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings
We study the classical weighted perfect matchings problem for bipartite ...
research
∙
01/17/2021
Membership Inference Attack on Graph Neural Networks
Graph Neural Networks (GNNs), which generalize traditional deep neural n...
research
∙
04/29/2020
Valid Explanations for Learning to Rank Models
Learning-to-rank (LTR) is a class of supervised learning techniques that...
research
∙
04/29/2020
Deep Reinforcement Learning with Graph-based State Representations
Deep RL approaches build much of their success on the ability of the dee...
research
∙
04/22/2020
Boilerplate Removal using a Neural Sequence Labeling Model
The extraction of main content from web pages is an important task for n...
research
∙
10/11/2019
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases
In this paper we propose and study the novel problem of explaining node ...
research
∙
03/19/2019
A Comprehensive Comparison of Unsupervised Network Representation Learning Methods
There has been appreciable progress in unsupervised network representati...
research
∙
12/07/2018
Asynchronous Training of Word Embeddings for Large Text Corpora
Word embeddings are a powerful approach for analyzing language and have ...
research
∙
10/22/2018
Node Representation Learning for Directed Graphs
We propose a novel approach for learning node representations in directe...
research
∙
07/17/2018