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 algorithms in healthcare applications: a systematic review
Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …
fundamental yet challenging task. Benefiting from the powerful representation capability of …
A scalable redefined stochastic blockmodel
Stochastic blockmodel (SBM) is a widely used statistical network representation model, with
good interpretability, expressiveness, generalization, and flexibility, which has become …
good interpretability, expressiveness, generalization, and flexibility, which has become …
Scaling attributed network embedding to massive graphs
Given a graph G where each node is associated with a set of attributes, attributed network
embedding (ANE) maps each node v∈ G to a compact vector Xv, which can be used in …
embedding (ANE) maps each node v∈ G to a compact vector Xv, which can be used in …
DVAEGMM: Dual variational autoencoder with gaussian mixture model for anomaly detection on attributed networks
A significant aspect of today's digital information is attributed networks, which combine
multiple node attributes with the basic network topology to extract knowledge. Anomaly …
multiple node attributes with the basic network topology to extract knowledge. Anomaly …
Lp-robin: link prediction in dynamic networks exploiting incremental node embedding
In many real-world domains, data can naturally be represented as networks. This is the case
of social networks, bibliographic networks, sensor networks and biological networks. Some …
of social networks, bibliographic networks, sensor networks and biological networks. Some …
Graph autoencoder with preserving node attribute similarity
M Lin, K Wen, X Zhu, H Zhao, X Sun - Entropy, 2023 - mdpi.com
The graph autoencoder (GAE) is a powerful graph representation learning tool in an
unsupervised learning manner for graph data. However, most existing GAE-based methods …
unsupervised learning manner for graph data. However, most existing GAE-based methods …
Graph representation learning based on deep generative gaussian mixture models
Graph representation learning is an effective tool for facilitating graph analysis with machine
learning methods. Most GNNs, including Graph Convolutional Networks (GCN), Graph …
learning methods. Most GNNs, including Graph Convolutional Networks (GCN), Graph …
DAEM: Deep attributed embedding based multi-task learning for predicting adverse drug–drug interaction
J Zhu, Y Liu, Y Zhang, Z Chen, K She, R Tong - Expert Systems with …, 2023 - Elsevier
Adverse drug–drug interaction (ADDI) is an important concern in pharmaceutical industry
and becomes a leading cause of morbidity and mortality in public health. With the increasing …
and becomes a leading cause of morbidity and mortality in public health. With the increasing …