Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Theberge, Francois"'
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 well as the rarity, of simplicial interactions in a hypergraph while th
Externí odkaz:
http://arxiv.org/abs/2408.11806
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 context of hypergraphs. We observe that a direct application of the Louv
Externí odkaz:
http://arxiv.org/abs/2406.17556
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 model generates graphs similar to the well-known LFR model but it is f
Externí odkaz:
http://arxiv.org/abs/2312.00238
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 define a family of community-aware node features and investigate their prop
Externí odkaz:
http://arxiv.org/abs/2311.04730
Publikováno v:
Kami\'nski, B., Pra{\l}at, P. & Th\'eberge, F. Artificial benchmark for community detection with outliers (ABCD+o). Appl Netw Sci 8, 25 (2023)
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 model generates graphs with similar properties as the well-known LFR o
Externí odkaz:
http://arxiv.org/abs/2301.05749
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. The model generates graphs with similar properties as
Externí odkaz:
http://arxiv.org/abs/2210.15009
Publikováno v:
In Theoretical Computer Science 12 February 2025 1026
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 community detection algorithms that are unsupervised by nature. We prop
Externí odkaz:
http://arxiv.org/abs/2203.14899
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 model generates graphs with similar properties as the well-known LFR o
Externí odkaz:
http://arxiv.org/abs/2203.01480
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 the existing survey articles are either much narrower in their scope o
Externí odkaz:
http://arxiv.org/abs/2112.07041