A scalable generative graph model with community structure
Network data is ubiquitous and growing, yet we lack realistic generative network models that
can be calibrated to match real-world data. The recently proposed block two-level Erdös …
can be calibrated to match real-world data. The recently proposed block two-level Erdös …
Testing for high‐dimensional geometry in random graphs
We study the problem of detecting the presence of an underlying high‐dimensional
geometric structure in a random graph. Under the null hypothesis, the observed graph is a …
geometric structure in a random graph. Under the null hypothesis, the observed graph is a …
Finding the hierarchy of dense subgraphs using nucleus decompositions
Finding dense substructures in a graph is a fundamental graph mining operation, with
applications in bioinformatics, social networks, and visualization to name a few. Yet most …
applications in bioinformatics, social networks, and visualization to name a few. Yet most …
Finding cliques in social networks: A new distribution-free model
We propose a new distribution-free model of social networks. Our definitions are motivated
by one of the most universal signatures of social networks, triadic closure---the property that …
by one of the most universal signatures of social networks, triadic closure---the property that …
Overlapping community detection in labeled graphs
We present a new approach for the problem of finding overlapping communities in graphs
and social networks. Our approach consists of a novel problem definition and three …
and social networks. Our approach consists of a novel problem definition and three …
Latent distance estimation for random geometric graphs
E Araya Valdivia, DC Yohann - Advances in Neural …, 2019 - proceedings.neurips.cc
Random geometric graphs are a popular choice for a latent points generative model for
networks. Their definition is based on a sample of $ n $ points $ X_1, X_2,\cdots, X_n $ on …
networks. Their definition is based on a sample of $ n $ points $ X_1, X_2,\cdots, X_n $ on …
Triangle-aware spectral sparsifiers and community detection
K Sotiropoulos, CE Tsourakakis - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
Triangle-aware graph partitioning has proven to be a successful approach to finding
communities in real-world data [8, 40, 51, 54]. But how can we explain its empirical success …
communities in real-world data [8, 40, 51, 54]. But how can we explain its empirical success …
A simple statistic for determining the dimensionality of complex networks
Detecting the dimensionality of graphs is a central topic in machine learning. While the
problem has been tackled empirically as well as theoretically, existing methods have several …
problem has been tackled empirically as well as theoretically, existing methods have several …
Nucleus decompositions for identifying hierarchy of dense subgraphs
Finding dense substructures in a graph is a fundamental graph mining operation, with
applications in bioinformatics, social networks, and visualization to name a few. Yet most …
applications in bioinformatics, social networks, and visualization to name a few. Yet most …
[HTML][HTML] Simplifying social networks via triangle-based cohesive subgraphs
One main challenge for simplifying node-link diagrams of large-scale social networks lies in
that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph …
that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph …