8 citations · 8 across the 4 of their papers we have counts for
5 papers
COLOGNE: Coordinated Local Graph Neighborhood Sampling
Konstantin Kutzkov
Representation learning for graphs enables the application of standard machine learning algorithms and data analysis tools to graph data. Replacing discrete unordered objects such…
Query-Efficient Correlation Clustering
David García-Soriano, Konstantin Kutzkov, Francesco Bonchi +1
Correlation clustering is arguably the most natural formulation of clustering. Given n objects and a pairwise similarity measure, the goal is to cluster the objects so that, to the…
KONG: Kernels for ordered-neighborhood graphs
Moez Draief, Konstantin Kutzkov, Kevin Scaman +1
We present novel graph kernels for graphs with node and edge labels that have ordered neighborhoods, i.e. when neighbor nodes follow an order. Graphs with ordered neighborhoods are…
Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be u…
On Parallelizing Matrix Multiplication by the Column-Row Method
Andrea Campagna, Konstantin Kutzkov, Rasmus Pagh
We consider the problem of sparse matrix multiplication by the column row method in a distributed setting where the matrix product is not necessarily sparse. We present a surprisin…