A survey of community detection approaches: From statistical modeling to deep learning
Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …
into multiple sub-structures to help reveal their latent functions. Community detection has …
Community detection in networks: A user guide
S Fortunato, D Hric - Physics reports, 2016 - Elsevier
Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …
science. Communities, or clusters, are usually groups of vertices having higher probability of …
Learning causally invariant representations for out-of-distribution generalization on graphs
Despite recent success in using the invariance principle for out-of-distribution (OOD)
generalization on Euclidean data (eg, images), studies on graph data are still limited …
generalization on Euclidean data (eg, images), studies on graph data are still limited …
Graph clustering with graph neural networks
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …
analysis tasks such as node classification and link prediction. However, important …
Graphvae: Towards generation of small graphs using variational autoencoders
M Simonovsky, N Komodakis - … and Machine Learning–ICANN 2018: 27th …, 2018 - Springer
Deep learning on graphs has become a popular research topic with many applications.
However, past work has concentrated on learning graph embedding tasks, which is in …
However, past work has concentrated on learning graph embedding tasks, which is in …
Constrained graph variational autoencoders for molecule design
Q Liu, M Allamanis… - Advances in neural …, 2018 - proceedings.neurips.cc
Graphs are ubiquitous data structures for representing interactions between entities. With an
emphasis on applications in chemistry, we explore the task of learning to generate graphs …
emphasis on applications in chemistry, we explore the task of learning to generate graphs …
[图书][B] Model-based clustering and classification for data science: with applications in R
Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …
leave open such central questions as: how many clusters are there? Which method should I …
Revisiting semi-supervised learning with graph embeddings
We present a semi-supervised learning framework based on graph embeddings. Given a
graph between instances, we train an embedding for each instance to jointly predict the …
graph between instances, we train an embedding for each instance to jointly predict the …
Community detection and stochastic block models: recent developments
E Abbe - Journal of Machine Learning Research, 2018 - jmlr.org
The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …
employed as a canonical model to study clustering and community detection, and provides …
Community detection in node-attributed social networks: a survey
P Chunaev - Computer Science Review, 2020 - Elsevier
Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …