Community detection in graphs
S Fortunato - Physics reports, 2010 - Elsevier
The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …
complex systems. One of the most relevant features of graphs representing real systems is …
Community detection in social media: Performance and application considerations
The proposed survey discusses the topic of community detection in the context of Social
Media. Community detection constitutes a significant tool for the analysis of complex …
Media. Community detection constitutes a significant tool for the analysis of complex …
EchoFakeD: improving fake news detection in social media with an efficient deep neural network
The increasing popularity of social media platforms has simplified the sharing of news
articles that have led to the explosion in fake news. With the emergence of fake news at a …
articles that have led to the explosion in fake news. With the emergence of fake news at a …
The development of social network analysis–with an emphasis on recent events
LC Freeman - The Sage handbook of social network analysis, 2011 - torrossa.com
In a recent book I reviewed the development of social network analysis from its earliest
beginnings until the late 1990s (Freeman, 2004). There, I characterized social network …
beginnings until the late 1990s (Freeman, 2004). There, I characterized social network …
Time to infer miRNA sponge modules
Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a
challenging and meaningful task for revealing ceRNA regulation mechanism at the module …
challenging and meaningful task for revealing ceRNA regulation mechanism at the module …
Multilevel local search algorithms for modularity clustering
R Rotta, A Noack - Journal of Experimental Algorithmics (JEA), 2011 - dl.acm.org
Modularity is a widely used quality measure for graph clusterings. Its exact maximization is
NP-hard and prohibitively expensive for large graphs. Popular heuristics first perform a …
NP-hard and prohibitively expensive for large graphs. Popular heuristics first perform a …
Column generation algorithms for exact modularity maximization in networks
Finding modules, or clusters, in networks currently attracts much attention in several
domains. The most studied criterion for doing so, due to Newman and Girvan [Phys. Rev. E …
domains. The most studied criterion for doing so, due to Newman and Girvan [Phys. Rev. E …
Multi-level algorithms for modularity clustering
A Noack, R Rotta - International symposium on experimental algorithms, 2009 - Springer
Modularity is a widely used quality measure for graph clusterings. Its exact maximization is
prohibitively expensive for large graphs. Popular heuristics progressively merge clusters …
prohibitively expensive for large graphs. Popular heuristics progressively merge clusters …
Modularity maximization in networks by variable neighborhood search.
Finding communities, or clusters, in networks, or graphs, has been the subject of intense
studies in the last ten years. The most used criterion for that purpose, despite some recent …
studies in the last ten years. The most used criterion for that purpose, despite some recent …
Loops and multiple edges in modularity maximization of networks
The modularity maximization model proposed by Newman and Girvan for the identification
of communities in networks works for general graphs possibly with loops and multiple …
of communities in networks works for general graphs possibly with loops and multiple …