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Found 14 papers, 9 papers with code
20 years of network community detection
A fundamental technical challenge in the analysis of network data is the automated discovery of communities - groups of nodes that are strongly connected or that share similar features or roles.
Robustness modularity in complex networks
Here we propose a new measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed.
Community detection in networks using graph embeddings
We compare their performance against that of traditional community detection algorithms.
Recency predicts bursts in the evolution of author citations
The citations process for scientific papers has been studied extensively.
Fast consensus clustering in complex networks
Algorithms for community detection are usually stochastic, leading to different partitions for different choices of random seeds.
Physics and Society Social and Information Networks Molecular Networks
Psychological and Personality Profiles of Political Extremists
Global recruitment into radical Islamic movements has spurred renewed interest in the appeal of political extremism.
Community detection in networks: A user guide
Community detection in networks is one of the most popular topics of modern network science.
Network structure, metadata and the prediction of missing nodes and annotations
The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure.
Benchmark model to assess community structure in evolving networks
In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure.
Physics and Society Social and Information Networks Data Analysis, Statistics and Probability
Community detection in networks: Structural communities versus ground truth
Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes.
Finding statistically significant communities in networks
In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics.
Community detection in graphs
The modern science of networks has brought significant advances to our understanding of complex systems.
Benchmark graphs for testing community detection algorithms
Community structure is one of the most important features of real networks and reveals the internal organization of the nodes.
Physics and Society Computational Physics
Scale-free network growth by ranking
Network growth is currently explained through mechanisms that rely on node prestige measures, such as degree or fitness.
Disordered Systems and Neural Networks Statistical Mechanics Physics and Society
