A comprehensive survey on deep graph representation learning

W Ju, Z Fang, Y Gu, Z Liu, Q Long, Z Qiao, Y Qin… - Neural Networks, 2024 - Elsevier
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Community detection algorithms in healthcare applications: a systematic review

M Rostami, M Oussalah, K Berahmand… - IEEE Access, 2023 - ieeexplore.ieee.org
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 …

A deep semi-supervised community detection based on point-wise mutual information

K Berahmand, Y Li, Y Xu - IEEE Transactions on Computational …, 2023 - ieeexplore.ieee.org
Network clustering is one of the fundamental unsupervised methods of knowledge
discovery. Its goal is to group similar nodes together without supervision or prior knowledge …

Detecting communities from heterogeneous graphs: A context path-based graph neural network model

L Luo, Y Fang, X Cao, X Zhang, W Zhang - Proceedings of the 30th ACM …, 2021 - dl.acm.org
Community detection, aiming to group the graph nodes into clusters with dense inner-
connection, is a fundamental graph mining task. Recently, it has been studied on the …

A deep learning approach for semi-supervised community detection in online social networks

A De Santo, A Galli, V Moscato, G Sperlì - Knowledge-Based Systems, 2021 - Elsevier
Abstract Social Network Analysis (SNA) has gained popularity as a way to unveil and
identify useful social patterns as communities among users. However the continuous …

Predictive analysis of hospital stay after caesarean section: a single-center study

AM Ponsiglione, TA Trunfio, F Amato, G Improta - Bioengineering, 2023 - mdpi.com
Caesarean section (CS) rate has seen a significant increase in recent years, especially in
industrialized countries. There are, in fact, several causes that justify a CS; however …

Osgnn: Original graph and subgraph aggregated graph neural network

Y Yan, C Li, Y Yu, X Li, Z Zhao - Expert Systems with Applications, 2023 - Elsevier
Abstract Heterogeneous Graph Embedding (HGE) is receiving a great attention from
researchers, as it can be widely and effectively used to solve problems from various real …

Community answer recommendation based on heterogeneous semantic fusion

Y Wu, H Yin, Q Zhou, J Dong, D Wei, D Liu - Expert Systems with …, 2024 - Elsevier
The community question-answering system has gradually replaced the search engine as the
primary way for people to acquire and share knowledge. Users' interactive behavior …

Similarity enhancement of heterogeneous networks by weighted incorporation of information

F Baharifard, V Motaghed - Knowledge and Information Systems, 2024 - Springer
In many real-world datasets, different aspects of information are combined, so the data is
usually represented as heterogeneous graphs whose nodes and edges have different types …

Community detection over feature-rich information networks: An eHealth case study

V Moscato, G Sperlì - Information Systems, 2022 - Elsevier
In this paper, we present a novel graph data model to analyze eating habits and physical
activities of a large number of persons, aiming at automatically detect groups of users …