1 code implementation • 8 Nov 2023 • Bogumił Kamiński, Paweł Prałat, François Théberge, Sebastian Zając
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.
1 code implementation • 13 Jan 2023 • Bogumił Kamiński, Paweł Prałat, François Théberge
The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes.
1 code implementation • 26 Oct 2022 • Bogumił Kamiński, Paweł Prałat, François Théberge
The Artificial Benchmark for Community Detection (ABCD) graph is a recently introduced random graph model with community structure and power-law distribution for both degrees and community sizes.
1 code implementation • 28 Mar 2022 • Bogumił Kamiński, Tomasz Olczak, Bartosz Pankratz, Paweł Prałat, François Théberge
We propose ABCDe, a multi-threaded implementation of the ABCD (Artificial Benchmark for Community Detection) graph generator.
no code implementations • 13 Dec 2021 • Stan Matwin, Aristides Milios, Paweł Prałat, Amilcar Soares, François Théberge
This survey draws a broad-stroke, panoramic picture of the State of the Art (SoTA) of the research in generative methods for the analysis of social media data.
2 code implementations • 30 Nov 2021 • Bogumił Kamiński, Łukasz Kraiński, Paweł Prałat, François Théberge
Graph embedding is a transformation of nodes of a network into a set of vectors.
2 code implementations • 16 Feb 2021 • Arash Dehghan-Kooshkghazi, Bogumił Kamiński, Łukasz Kraiński, Paweł Prałat, François Théberge
Graph embedding is a transformation of nodes of a graph into a set of vectors.
2 code implementations • 14 Jan 2020 • Bogumił Kamiński, Paweł Prałat, François Théberge
It is therefore important to test these algorithms for various scenarios that can only be done using synthetic graphs that have built-in community structure, power-law degree distribution, and other typical properties observed in complex networks.
2 code implementations • 19 Mar 2019 • Valérie Poulin, François Théberge
We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering.
3 code implementations • 14 Sep 2018 • Valérie Poulin, François Théberge
We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph.
2 code implementations • 29 Jun 2018 • Valérie Poulin, François Théberge
In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account.