83 citations · 230 across the 23 of their papers we have counts for
24 papers
A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers
Ryan DeWolfe, Paweł Prałat, François Théberge
Clustering algorithms are an essential part of the unsupervised data science ecosystem, and extrinsic evaluation of clustering algorithms requires a method for comparing the detect…
The Needle is a Thread: Finding Planted Paths in Noisy Process Trees
Maya Le, Paweł Prałat, Aaron Smith +1
Motivated by applications in cybersecurity such as finding meaningful sequences of malware-related events buried inside large amounts of computer log data, we introduce the "plante…
Multilayer Artificial Benchmark for Community Detection (mABCD)
Łukasz Kraiński, Michał Czuba, Piotr Bródka +3
One of the most persistent challenges in network science is the development of various synthetic graph models to support subsequent analyses. Among the most notable frameworks addr…
Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Ryan DeWolfe, François Théberge
Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge…
The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+)
Jordan Barrett, Ryan DeWolfe, Bogumił Kamiński +3
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…
Network Embedding Exploration Tool (NEExT)
Ashkan Dehghan, Paweł Prałat, François Théberge
Many real-world and artificial systems and processes can be represented as graphs. Some examples of such systems include social networks, financial transactions, supply chains, and…
Improving community detection via community association strength scores
Jordan Barrett, Ryan DeWolfe, Bogumił Kamiński +3
Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms ge…
Counting simplicial pairs in hypergraphs
Jordan Barrett, Paweł Prałat, Aaron Smith +1
We present two ways to measure the simplicial nature of a hypergraph: the simplicial ratio and the simplicial matrix. We show that the simplicial ratio captures the frequency, as w…
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…
Artificial Benchmark for Community Detection with Outliers (ABCD+o)
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. The…
Hypergraph Artificial Benchmark for Community Detection (h-ABCD)
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 c…
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…
Properties and Performance of the ABCDe Random Graph Model with Community Structure
Bogumił Kamiński, Tomasz Olczak, Bartosz Pankratz +2
In this paper, we investigate properties and performance of synthetic random graph models with a built-in community structure. Such models are important for evaluating and tuning c…
Survey of Generative Methods for Social Media Analysis
Stan Matwin, Aristides Milios, Paweł Prałat +2
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. It fills a void, as…
A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs
Bogumił Kamiński, Łukasz Kraiński, Paweł Prałat +1
Graph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relations…
Evaluating Node Embeddings of Complex Networks
Arash Dehghan-Kooshkghazi, Bogumił Kamiński, Łukasz Kraiński +2
Graph embedding is a transformation of nodes of a graph into a set of vectors. A~good embedding should capture the graph topology, node-to-node relationship, and other relevant inf…
Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure
Bogumił Kamiński, Paweł Prałat, François Théberge
Most of the current complex networks that are of interest to practitioners possess a certain community structure that plays an important role in understanding the properties of the…
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…
Ensemble Clustering for Graphs: Comparisons and Applications
Valérie Poulin, François Théberge
We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering. We validated our approach by replicating a study comparing g…
Clustering via Hypergraph Modularity
Bogumil Kaminski, Valerie Poulin, Pawel Pralat +2
Despite the fact that many important problems (including clustering) can be described using hypergraphs, theoretical foundations as well as practical algorithms using hypergraphs a…
Ensemble Clustering for Graphs
Valérie Poulin, François Théberge
We propose an ensemble clustering algorithm for graphs (ECG), which is based on the Louvain algorithm and the concept of consensus clustering. We validate our approach by replicati…
Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures
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. These graph-aware measures are alternatives to using set…