Search Results for author:
Found 11 papers, 10 papers with code
Predicting Properties of Nodes via Community-Aware Features
A community structure that is often present in complex networks plays an important role not only in their formation but also shapes dynamics of these networks, affecting properties of their nodes.
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.
Survey of Generative Methods for Social Media Analysis
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.
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.
Ensemble Clustering for Graphs: Comparisons and Applications
We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering.
Ensemble Clustering for Graphs
We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph.
Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures
In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account.
