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 …
Community detection in node-attributed social networks: a survey
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 …
Persistence homology of networks: methods and applications
Abstract Information networks are becoming increasingly popular to capture complex
relationships across various disciplines, such as social networks, citation networks, and …
relationships across various disciplines, such as social networks, citation networks, and …
[HTML][HTML] A comprehensive survey of text classification techniques and their research applications: Observational and experimental insights
The exponential growth of textual data presents substantial challenges in management and
analysis, notably due to high storage and processing costs. Text classification, a vital aspect …
analysis, notably due to high storage and processing costs. Text classification, a vital aspect …
Fine-grained attributed graph clustering
Graph clustering is a prevalent issue associated with social networks, data mining, and
machine learning; its objective is to detect communities or groups in networks. Inspired by …
machine learning; its objective is to detect communities or groups in networks. Inspired by …
Grace: A general graph convolution framework for attributed graph clustering
Attributed graph clustering (AGC) is an important problem in graph mining as more and
more complex data in real-world have been represented in graphs with attributed nodes …
more complex data in real-world have been represented in graphs with attributed nodes …
A novel simplicial complex representation of social media texts: The case of Twitter
With the rise in technological advances in the last decades, there has been an increase in
the complexity of the many systems that we are members of. The emergence and …
the complexity of the many systems that we are members of. The emergence and …
[HTML][HTML] A novel attributed community detection by integration of feature weighting and node centrality
Community detection is one of the primary problems in social network analysis and this
problem has more challenges in attributed social networks. The purpose of community …
problem has more challenges in attributed social networks. The purpose of community …
H-Louvain: Hierarchical Louvain-based community detection in social media data streams
The rise of online social networks has fundamentally transformed the traditional way of
social interaction and information dissemination, leading to a growing interest in precise …
social interaction and information dissemination, leading to a growing interest in precise …
Community detection in social networks by spectral embedding of typed graphs
Although there is considerable disagreement about the details, community detection in
social networks requires finding groups of nodes that are similar to one another, and …
social networks requires finding groups of nodes that are similar to one another, and …