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Found 23 papers, 20 papers with code
OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes
Duplication of nodes is a common problem encountered when building knowledge graphs (KGs) from heterogeneous datasets, where it is crucial to be able to merge nodes having the same meaning.
TigerLily: Finding drug interactions in silico with the Graph
Tigerlily is a TigerGraph based system designed to solve the drug interaction prediction task.
Synthetic Graph Generation to Benchmark Graph Learning
This shockingly small sample size (~10) allows for only limited scientific insight into the problem.
Continual and Sliding Window Release for Private Empirical Risk Minimization
One version of the algorithm uses the entire data history to improve the model for the recent window.
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
While many networks are signed or directed, or both, there is a lack of unified software packages on graph neural networks (GNNs) specially designed for signed and directed networks.
The Shapley Value in Machine Learning
Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning.
ChemicalX: A Deep Learning Library for Drug Pair Scoring
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task.
Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs
For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent.
A Unified View of Relational Deep Learning for Drug Pair Scoring
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed.
MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy
We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining.
The Shapley Value of Classifiers in Ensemble Games
We argue that the Shapley value of models in these games is an effective decision metric for choosing a high performing subset of models from the ensemble.
Pathfinder Discovery Networks for Neural Message Passing
Additional results from a challenging suite of node classification experiments show how PDNs can learn a wider class of functions than existing baselines.
Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent
Private machine learning involves addition of noise while training, resulting in lower accuracy.
Little Ball of Fur: A Python Library for Graph Sampling
In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms.
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.
Fast Sequence-Based Embedding with Diffusion Graphs
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.
Multi-scale Attributed Node Embedding
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.
Fast Sequence Based Embedding with Diffusion Graphs
A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves proper-ties such as distances between nodes.
GEMSEC: Graph Embedding with Self Clustering
In this paper we propose GEMSEC a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their features.
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