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no code implementations • 14 Mar 2022 • Peyman Afshani, Mark De Berg, Kevin Buchin, Jie Gao, Maarten Loffler, Amir Nayyeri, Benjamin Raichel, Rik Sarkar, Haotian Wang, Hao-Tsung Yang

For the Euclidean version of the problem, for instance, combining our results with known results on Euclidean TSP, yields a PTAS for approximating an optimal cyclic solution, and it yields a $(2(1-1/k)+\varepsilon)$-approximation of the optimal unrestricted solution.

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 • 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.

3 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.

1 code implementation • 16 Feb 2021 • Gabriele Beltramo, Rayna Andreeva, Ylenia Giarratano, Miguel O. Bernabeu, Rik Sarkar, Primoz Skraba

While topological data analysis of higher-dimensional parameter spaces using stronger invariants such as homology continues to be the subject of intense research, Euler characteristic is more manageable theoretically and computationally, and this analysis can be seen as an important intermediary step in multi-parameter topological data analysis.

Topological Data Analysis Algebraic Topology Computational Geometry

3 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 • 14 Aug 2020 • Haotian Wang, Abhirup Ghosh, Jiaxin Ding, Rik Sarkar, Jie Gao

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus.

Social and Information Networks Physics and Society

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.

no code implementations • 5 May 2020 • Peyman Afshani, Mark De Berg, Kevin Buchin, Jie Gao, Maarten Loffler, Amir Nayyeri, Benjamin Raichel, Rik Sarkar, Haotian Wang, Hao-Tsung Yang

The problem is NP-hard, as it has the traveling salesman problem as a special case (when $k=1$ and all sites have the same weight).

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.

Ranked #1 on Community Detection on Facebook Athletes

Social and Information Networks

no code implementations • 3 Nov 2014 • Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause

Such problems can often be reduced to maximizing a submodular set function subject to various constraints.

no code implementations • NeurIPS 2013 • Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause

Such problems can often be reduced to maximizing a submodular set function subject to cardinality constraints.

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