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.
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.
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
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining.
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.
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus.
Social and Information Networks Physics and Society
Private machine learning involves addition of noise while training, resulting in lower accuracy.
In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms.
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.
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).
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.
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.
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.
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.
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
Such problems can often be reduced to maximizing a submodular set function subject to cardinality constraints.