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Found 7 papers, 7 papers with code
Predicting Properties of Nodes via Community-Aware Features
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.
Artificial Benchmark for Community Detection with Outliers (ABCD+o)
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.
Hypergraph Artificial Benchmark for Community Detection (h-ABCD)
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.
Properties and Performance of the ABCDe Random Graph Model with Community Structure
We propose ABCDe, a multi-threaded implementation of the ABCD (Artificial Benchmark for Community Detection) graph generator.
A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs
Graph embedding is a transformation of nodes of a network into a set of vectors.
Evaluating Node Embeddings of Complex Networks
Graph embedding is a transformation of nodes of a graph into a set of vectors.
Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure
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.
