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Found 5 papers, 3 papers with code
LoNe Sampler: Graph node embeddings by coordinated local neighborhood sampling
Local graph neighborhood sampling is a fundamental computational problem that is at the heart of algorithms for node representation learning.
COLOGNE: Coordinated Local Graph Neighborhood Sampling
We address this shortcoming and consider the problem of learning discrete node embeddings such that the coordinates of the node vector representations are graph nodes.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Query-Efficient Correlation Clustering
Up to constant factors, our algorithm yields a provably optimal trade-off between the number of queries $Q$ and the worst-case error attained, even for adaptive algorithms.
KONG: Kernels for ordered-neighborhood graphs
We present novel graph kernels for graphs with node and edge labels that have ordered neighborhoods, i. e. when neighbor nodes follow an order.
Learning Convolutional Neural Networks for Graphs
Numerous important problems can be framed as learning from graph data.
Ranked #3 on
Graph Classification
on COX2
