Community detection in large hypergraphs. [PDF]
Sci Adv, 2023 Hypergraphs, describing networks where interactions take place among any number of units, are a natural tool to model many real-world social and biological systems. Here, we propose a principled framework to model the organization of higher-order data.
Ruggeri N +3 more
europepmc +7 more sources
A Community Detection Model Based on Dynamic Propagation-Aware Multi-Hop Feature Aggregation [PDF]
EntropyCommunity detection is a crucial technique for uncovering latent network structures, analyzing group behaviors, and understanding information dissemination pathways.
Chao Lei +5 more
doaj +2 more sources
A Stochastic Approach to Generalized Modularity Based Community Detection [PDF]
EntropyWe study a stochastic approach to generalized modularity-based community detection by comparing two variants of the aforementioned approach to the standard modularity-based approach. In particular, we compare means and distributions. We also confirm that
James Tipton, Jordan Langston
doaj +2 more sources
Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning (RenEEL) [PDF]
EntropyArguably, the most fundamental problem in Network Science is finding structure within a complex network. Often, this is achieved by partitioning the network’s nodes into communities in a way that maximizes an objective function.
Tania Ghosh +2 more
doaj +2 more sources
A Comprehensive Survey on Community Detection With Deep Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis.
Xing Su +11 more
semanticscholar +1 more source
A hybrid method for community detection based on user interactions, topology and frequent pattern mining [PDF]
مجله مدل سازی در مهندسی, 2023 In recent years, community detection in social networks has become one of the most important research areas. One of the ways to community detection is to use interactions between users. There are different types of interactions in social networks, which,
Somaye Sayari +2 more
doaj +1 more source
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions.
Di Jin +5 more
semanticscholar +1 more source
A Comprehensive Review of Community Detection in Graphs [PDF]
Neurocomputing, 2023 The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs.
Songlai Ning, Jiakang Li, Y. Lu
semanticscholar +1 more source
Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction [PDF]
Neural Networks, 2022 Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection problems where, according to recent and concurring experimental evaluations,
Guillaume Salha-Galvan +4 more
semanticscholar +1 more source
20 years of network community detection [PDF]
Nature Physics, 2022 A fundamental technical challenge in the analysis of network data is the automated discovery of communities — groups of nodes that are strongly connected or that share similar features or roles.
S. Fortunato, M. Newman
semanticscholar +1 more source