808 citations · 856 across the 12 of their papers we have counts for
12 papers
Learning Graph Node Embeddings by Smooth Pair Sampling
Konstantin Kutzkov
Random walk-based node embedding algorithms have attracted a lot of attention due to their scalability and ease of implementation. Previous research has focused on different walk s…
LoNe Sampler: Graph node embeddings by coordinated local neighborhood sampling
Konstantin Kutzkov
Local graph neighborhood sampling is a fundamental computational problem that is at the heart of algorithms for node representation learning. Several works have presented algorithm…
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…
Consistent Subset Sampling
Konstantin Kutzkov, Rasmus Pagh
Consistent sampling is a technique for specifying, in small space, a subset of a potentially large universe such that the elements in satisfy a suitably chosen sampling…
Triangle counting in dynamic graph streams
Laurent Bulteau, Vincent Froese, Konstantin Kutzkov +1
Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been st…
Local correlation clustering
Francesco Bonchi, David García-Soriano, Konstantin Kutzkov
Correlation clustering is perhaps the most natural formulation of clustering. Given objects and a pairwise similarity measure, the goal is to cluster the objects so that, to th…
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…
Deterministic algorithms for skewed matrix products
Konstantin Kutzkov
Recently, Pagh presented a randomized approximation algorithm for the multiplication of real-valued matrices building upon work for detecting the most frequent items in data stream…
Using CSP To Improve Deterministic 3-SAT
Konstantin Kutzkov, Dominik Scheder
We show how one can use certain deterministic algorithms for higher-value constraint satisfaction problems (CSPs) to speed up deterministic local search for 3-SAT. This way, we imp…