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Found 27 papers, 13 papers with code
Model Selection with Model Zoo via Graph Learning
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i. e., model zoos.
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Dual-encoder-based dense retrieval models have become the standard in IR.
DINE: Dimensional Interpretability of Node Embeddings
Towards that, we first develop new metrics that measure the global interpretability of embedding vectors based on the marginal contribution of the embedding dimensions to predicting graph structure.
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?
Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education.
Multi-label Node Classification On Graph-Structured Data
As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets.
Privacy and Transparency in Graph Machine Learning: A Unified Perspective
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 models and their applications in several domains.
Private Graph Extraction via Feature Explanations
Based on the different kinds of auxiliary information available to the adversary, we propose several graph reconstruction attacks.
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
Given a GNN model, several interpretability approaches exist to explain GNN models with diverse (sometimes conflicting) evaluation methodologies.
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions
We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets.
Efficient Neural Ranking using Forward Indexes
In this paper, we propose the Fast-Forward index -- a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores -- as a replacement for contextual re-rankers and dense indexes based on nearest neighbor search.
Knowledge-Aware Neural Networks for Medical Forum Question Classification
Online medical forums have become a predominant platform for answering health-related information needs of consumers.
Releasing Graph Neural Networks with Differential Privacy Guarantees
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 trained GNNs.
MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction
To effectively test the generalization capability of our model, we construct large-scale experiments on standard benchmark datasets as well as our proposed larger independent test sets and case studies.
Learnt Sparsification for Interpretable Graph Neural Networks
However, GNNs remain hard to interpret as the interplay between node features and graph structure is only implicitly learned.
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
In this paper, we lay down some of the fundamental principles that an explanation method for graph neural networks should follow and introduce a metric RDT-Fidelity as a measure of the explanation's effectiveness.
Achieving differential privacy for $k$-nearest neighbors based outlier detection by data partitioning
When applying outlier detection in settings where data is sensitive, mechanisms which guarantee the privacy of the underlying data are needed.
A Review of Anonymization for Healthcare Data
Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, reduced cost, and it drives innovative solutions within the healthcare sector.
Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings
We study the classical weighted perfect matchings problem for bipartite graphs or sometimes referred to as the assignment problem, i. e., given a weighted bipartite graph $G = (U\cup V, E)$ with weights $w : E \rightarrow \mathcal{R}$ we are interested to find the maximum matching in $G$ with the minimum/maximum weight.
Data Structures and Algorithms Discrete Mathematics Combinatorics
Membership Inference Attack on Graph Neural Networks
While choosing the simplest possible attack model that utilizes the posteriors of the trained model (black-box access), we thoroughly analyze the properties of GNNs and the datasets which dictate the differences in their robustness towards MI attack.
Hard Masking for Explaining Graph Neural Networks
Graph Neural Networks (GNNs) are a flexible and powerful family of models that build nodes' representations on irregular graph-structured data.
Graph-based State Representation for Deep Reinforcement Learning
Motivated by the recent success of node representations for several graph analytical tasks we specifically investigate the capability of node representation learning methods to effectively encode the topology of the underlying MDP in Deep RL.
Valid Explanations for Learning to Rank Models
In extensive quantitative experiments we show that our approach outperforms other model agnostic explanation approaches across pointwise, pairwise and listwise LTR models in validity while not compromising on completeness.
Boilerplate Removal using a Neural Sequence Labeling Model
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, to information retrieval or natural language processing.
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases
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 representation embeddings.
A Comparative Study for Unsupervised Network Representation Learning
However, there is no common ground for systematic comparison of embeddings to understand their behavior for different graphs and tasks.
Asynchronous Training of Word Embeddings for Large Text Corpora
In this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings.
Node Representation Learning for Directed Graphs
We also unearth the limitations of evaluations on directed graphs in previous works and propose a clear strategy for evaluating link prediction and graph reconstruction in directed graphs.
