1 code implementation • 5 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.
1 code implementation • 18 Apr 2022 • Benedek Rozemberczki
Tigerlily is a TigerGraph based system designed to solve the drug interaction prediction task.
1 code implementation • 4 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.
no code implementations • 7 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.
1 code implementation • 22 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.
2 code implementations • 11 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.
1 code implementation • 10 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.
no code implementations • 20 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.
3 code implementations • 4 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.
2 code implementations • 28 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.
4 code implementations • 15 Apr 2021 • Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, Rik Sarkar
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
4 code implementations • 16 Feb 2021 • Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, Tamas Ferenci
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
2 code implementations • 8 Jan 2021 • Benedek Rozemberczki, Rik Sarkar
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining.
2 code implementations • 6 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.
1 code implementation • 24 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.
no code implementations • 25 Jun 2020 • Lauren Watson, Benedek Rozemberczki, Rik Sarkar
Private machine learning involves addition of noise while training, resulting in lower accuracy.
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.
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.
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.
4 code implementations • 21 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.
4 code implementations • 28 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.
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.
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.
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