Search Results for author:
Found 6 papers, 2 papers with code
Dissecting graph measure performance for node clustering in LFR parameter space
While usually measure comparisons are limited to general measure ranking on a particular dataset, we aim to explore the performance of various measures depending on graph features.
Dissecting graph measures performance for node clustering in LFR parameter space
Given particular graph parameters, this allows us to choose the best measure to use for clustering.
How to choose the most appropriate centrality measure? A decision tree approach
To address this, we introduce the culling method, which relies on the expert concept of centrality behavior on simple graphs.
Do logarithmic proximity measures outperform plain ones in graph clustering?
A possible origin of this effect is that most kernels have a multiplicative nature, while the nature of distances used in cluster algorithms is an additive one (cf.
Semi-supervised Learning with Regularized Laplacian
We study a semi-supervised learning method based on the similarity graph and RegularizedLaplacian.
Matrices of forests, analysis of networks, and ranking problems
The matrices of spanning rooted forests are studied as a tool for analysing the structure of networks and measuring their properties.
