100 citations · 371 across the 26 of their papers we have counts for
17 papers · 1 filter
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?
Iyiola E. Olatunji, Anmar Hizber, Oliver Sihlovec +1
Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education. However, recent studies have shown t…
Multi-label Node Classification On Graph-Structured Data
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic +1
Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-cla…
Privacy and Transparency in Graph Machine Learning: A Unified Perspective
Megha Khosla
Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of mod…
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
Mandeep Rathee, Thorben Funke, Avishek Anand +1
The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with gra…
Private Graph Extraction via Feature Explanations
Iyiola E. Olatunji, Mandeep Rathee, Thorben Funke +1
Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through…
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions
Thi Ngan Dong, Graham Brogden, Gisa Gerold +1
Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial rol…
Releasing Graph Neural Networks with Differential Privacy Guarantees
Iyiola E. Olatunji, Thorben Funke, Megha Khosla
With the increasing popularity of graph neural networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over the privacy aspects of…
Learnt Sparsification for Interpretable Graph Neural Networks
Mandeep Rathee, Zijian Zhang, Thorben Funke +2
Graph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as ind…
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
Thorben Funke, Megha Khosla, Mandeep Rathee +1
With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network. E…
Achieving differential privacy for -nearest neighbors based outlier detection by data partitioning
Jens Rauch, Iyiola E. Olatunji, Megha Khosla
When applying outlier detection in settings where data is sensitive, mechanisms which guarantee the privacy of the underlying data are needed. The -nearest neighbors (-NN) al…
Membership Inference Attack on Graph Neural Networks
Iyiola E. Olatunji, Wolfgang Nejdl, Megha Khosla
Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focu…
Valid Explanations for Learning to Rank Models
Jaspreet Singh, Zhenye Wang, Megha Khosla +1
Learning-to-rank (LTR) is a class of supervised learning techniques that apply to ranking problems dealing with a large number of features. The popularity and widespread applicatio…
Boilerplate Removal using a Neural Sequence Labeling Model
Jurek Leonhardt, Avishek Anand, Megha Khosla
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, t…
Graph-based State Representation for Deep Reinforcement Learning
Vikram Waradpande, Daniel Kudenko, Megha Khosla
Deep RL approaches build much of their success on the ability of the deep neural network to generate useful internal representations. Nevertheless, they suffer from a high sample-c…
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases
Maximilian Idahl, Megha Khosla, Avishek Anand
In this paper we propose and study the novel problem of explaining node embeddings by finding embedded human interpretable subspaces in already trained unsupervised node representa…
A Comparative Study for Unsupervised Network Representation Learning
Megha Khosla, Vinay Setty, Avishek Anand
There has been appreciable progress in unsupervised network representation learning (UNRL) approaches over graphs recently with flexible random-walk approaches, new optimization ob…
Asynchronous Training of Word Embeddings for Large Text Corpora
Avishek Anand, Megha Khosla, Jaspreet Singh +2
Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over hug…