375 citations · 515 across the 19 of their papers we have counts for
23 papers
OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes
David Geleta, Andriy Nikolov, Mark ODonoghue +4
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…
TigerLily: Finding drug interactions in silico with the Graph
Benedek Rozemberczki
Tigerlily is a TigerGraph based system designed to solve the drug interaction prediction task. In this machine learning task, we want to predict whether two drugs have an adverse i…
Synthetic Graph Generation to Benchmark Graph Learning
Anton Tsitsulin, Benedek Rozemberczki, John Palowitch +1
Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, bec…
Continual and Sliding Window Release for Private Empirical Risk Minimization
Lauren Watson, Abhirup Ghosh, Benedek Rozemberczki +1
It is difficult to continually update private machine learning models with new data while maintaining privacy. Data incur increasing privacy loss -- as measured by differential pri…
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
Yixuan He, Xitong Zhang, Junjie Huang +3
Networks are ubiquitous in many real-world applications (e.g., social networks encoding trust/distrust relationships, correlation networks arising from time series data). While man…
The Shapley Value in Machine Learning
Benedek Rozemberczki, Lauren Watson, Péter Bayer +4
Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss fu…
ChemicalX: A Deep Learning Library for Drug Pair Scoring
Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva +9
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. The prim…
A Unified View of Relational Deep Learning for Drug Pair Scoring
Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov +3
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design ta…
Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs
Gavin Edwards, Sebastian Nilsson, Benedek Rozemberczki +1
For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent. In other domains, a neurosymboli…
MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy
Benedek Rozemberczki, Anna Gogleva, Sebastian Nilsson +3
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
Benedek Rozemberczki, Paul Scherer, Yixuan He +8
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of…
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Benedek Rozemberczki, Paul Scherer, Oliver Kiss +2
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectur…
Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings
Benedek Rozemberczki, Rik Sarkar
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining. Novel node embedding techniques are often tested on a restrict…
The Shapley Value of Classifiers in Ensemble Games
Benedek Rozemberczki, Rik Sarkar
What is the value of an individual model in an ensemble of binary classifiers? We answer this question by introducing a class of transferable utility cooperative games called \text…
Pathfinder Discovery Networks for Neural Message Passing
Benedek Rozemberczki, Peter Englert, Amol Kapoor +2
In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model…
Scaling Graph Neural Networks with Approximate PageRank
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi +5
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge - many recently propos…
Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent
Lauren Watson, Benedek Rozemberczki, Rik Sarkar
Private machine learning involves addition of noise while training, resulting in lower accuracy. Intuitively, greater stability can imply greater privacy and improve this privacy-u…
Little Ball of Fur: A Python Library for Graph Sampling
Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our go…
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
Benedek Rozemberczki, Rik Sarkar
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. We introduce F…
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
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. The primary goal of t…
Fast Sequence-Based Embedding with Diffusion Graphs
Benedek Rozemberczki, Rik Sarkar
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based…
Multi-scale Attributed Node Embedding
Benedek Rozemberczki, Carl Allen, Rik Sarkar
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…
GEMSEC: Graph Embedding with Self Clustering
Benedek Rozemberczki, Ryan Davies, Rik Sarkar +1
Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC -- a graph embedding algorithm which learns a clustering o…