8 citations · 8 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…