10 papers
A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers
Ryan DeWolfe, PaweÅ PraÅat, 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, PaweÅ PraÅat, 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…
The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+)
Jordan Barrett, Ryan DeWolfe, BogumiÅ KamiÅski +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…
Multilayer Artificial Benchmark for Community Detection (mABCD)
Åukasz KraiÅski, MichaÅ 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…
Network Embedding Exploration Tool (NEExT)
Ashkan Dehghan, PaweÅ PraÅat, 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…