3 citations · 6 across the 4 of their papers we have counts for
7 papers · 1 filter
Modularity Based Community Detection in Hypergraphs
Bogumił Kamiński, Paweł Misiorek, Paweł Prałat +1
In this paper, we propose a scalable community detection algorithm using hypergraph modularity function, h-Louvain. It is an adaptation of the classical Louvain algorithm in the co…
Self-similarity of Communities of the ABCD Model
Jordan Barrett, Bogumil Kaminski, Pawel Pralat +1
The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The…
Predicting Properties of Nodes via Community-Aware Features
Bogumił Kamiński, Paweł Prałat, François Théberge +1
This paper shows how information about the network's community structure can be used to define node features with high predictive power for classification tasks. To do so, we defin…
Modularity of the ABCD Random Graph Model with Community Structure
Bogumil Kaminski, Bartosz Pankratz, Pawel Pralat +1
The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The…
On Broadcasting Time in the Model of Travelling Agents
Reaz Huq, Bogumil Kaminski, Atefeh Mashatan +2
Consider the following broadcasting process run on a connected graph . Suppose that agents start on vertices selected from uniformly and independently at ran…
An Unsupervised Framework for Comparing Graph Embeddings
Bogumil Kaminski, Pawel Pralat, Francois Theberge
Graph embedding is a transformation of vertices of a graph into set of vectors. Good embeddings should capture the graph topology, vertex-to-vertex relationship, and other relevant…