Search Results for author: Benedek Rozemberczki

Found 23 papers, 20 papers with code

OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes

1 code implementation5 Jun 2022 David Geleta, Andriy Nikolov, Mark ODonoghue, Benedek Rozemberczki, Anna Gogleva, Valentina Tamma, Terry R. Payne

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.

Knowledge Graphs

TigerLily: Finding drug interactions in silico with the Graph

1 code implementation18 Apr 2022 Benedek Rozemberczki

Tigerlily is a TigerGraph based system designed to solve the drug interaction prediction task.

Graph Mining

Synthetic Graph Generation to Benchmark Graph Learning

1 code implementation4 Apr 2022 Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi

This shockingly small sample size (~10) allows for only limited scientific insight into the problem.

Graph Generation Graph Learning +2

Continual and Sliding Window Release for Private Empirical Risk Minimization

no code implementations7 Mar 2022 Lauren Watson, Abhirup Ghosh, Benedek Rozemberczki, Rik Sarkar

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

1 code implementation22 Feb 2022 Yixuan He, Xitong Zhang, JunJie Huang, Benedek Rozemberczki, Mihai Cucuringu, Gesine Reinert

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.

Time Series Time Series Analysis

The Shapley Value in Machine Learning

2 code implementations11 Feb 2022 Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Olivér Kiss, Sebastian Nilsson, Rik Sarkar

Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning.

BIG-bench Machine Learning Data Valuation +5

ChemicalX: A Deep Learning Library for Drug Pair Scoring

1 code implementation10 Feb 2022 Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, Tyler Derr, Benjamin M Gyori

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.

BIG-bench Machine Learning

Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

no code implementations20 Nov 2021 Gavin Edwards, Sebastian Nilsson, Benedek Rozemberczki, Eliseo Papa

For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent.

Drug Discovery Knowledge Graphs +2

A Unified View of Relational Deep Learning for Drug Pair Scoring

3 code implementations4 Nov 2021 Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov, Michael Ughetto, Sebastian Nilsson, Eliseo Papa

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.

BIG-bench Machine Learning

MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy

2 code implementations28 Oct 2021 Benedek Rozemberczki, Anna Gogleva, Sebastian Nilsson, Gavin Edwards, Andriy Nikolov, Eliseo Papa

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.

Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

2 code implementations8 Jan 2021 Benedek Rozemberczki, Rik Sarkar

Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining.

Graph Mining Node Classification

The Shapley Value of Classifiers in Ensemble Games

2 code implementations6 Jan 2021 Benedek Rozemberczki, Rik Sarkar

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.

Ensemble Pruning Graph Classification

Pathfinder Discovery Networks for Neural Message Passing

1 code implementation24 Oct 2020 Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, Bryan Perozzi

Additional results from a challenging suite of node classification experiments show how PDNs can learn a wider class of functions than existing baselines.

Graph Attention Node Classification +1

Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent

no code implementations25 Jun 2020 Lauren Watson, Benedek Rozemberczki, Rik Sarkar

Private machine learning involves addition of noise while training, resulting in lower accuracy.

feature selection

Little Ball of Fur: A Python Library for Graph Sampling

1 code implementation CIKM 2020 Benedek Rozemberczki, Oliver Kiss, Rik Sarkar

In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms.

Graph Classification Graph Embedding +2

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

3 code implementations CIKM 2020 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.

Graph Classification Node Classification +1

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

2 code implementations CIKM 2020 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.

BIG-bench Machine Learning Clustering +5

Fast Sequence-Based Embedding with Diffusion Graphs

4 code implementations21 Jan 2020 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.

Clustering Community Detection +2

Multi-scale Attributed Node Embedding

4 code implementations28 Sep 2019 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 approach similar to Skip-gram.

Network Embedding

Fast Sequence Based Embedding with Diffusion Graphs

2 code implementations CompleNet 2018 Benedek Rozemberczki, Rik Sarkar

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.

Clustering Community Detection +3

GEMSEC: Graph Embedding with Self Clustering

3 code implementations ASONAM 2019 Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton

In this paper we propose GEMSEC a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their features.

Social and Information Networks

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