Ahui Hu’s scientific contributions

... Node-based algorithms focus on utilizing the structural information of nodes to perform community detection. These methods include well-known algorithms Zhao et al. 2024;) that optimize local benefit functions. In addition to structural clustering, several approaches have explored node influence and importance ranking as critical factors in guiding community detection, including gravity-inspired ranking models (Yi-Run et al. 2022) and dual-perspective influence scoring like Globaland-Local centrality based on clustering algorithm (GLC) (Ruan et al. 2024). ...

Reference:

Common-neighbor based overlapping community detection in complex networks

... Non-negative matrix factorization (NMF) based methods [9,10] are widely accepted because not only do they consider community membership but also they are simple and effective. They show their efficiency in various types of community detection tasks like overlapping communities, communities in attributed networks and multiplex networks. ...

Reference:

Attributed Network Clustering through Mutual Affinity Propagated Non-negative Matrix Factorization

... Recently, NMF algorithms have been widely used in different types of networks to perform link prediction tasks, mainly due to the advantages of NMF such as dimensionality reduction, interpretability and network reconfiguration [28]. The core idea of most of the published NMF-based link prediction models is to map the adjacency matrix of the network to a low-dimensional latent space, then maintain the network structural information by graph regularization, and finally reconstruct the original network with minimum error [29][30][31]. Wang et al. [29] proposed a non-negative matrix factorization model based on the kernel framework, which preserves both local and global network structure information. Mahmoodi et al. [30] proposed the adversarial nonnegative matrix factorization link prediction model, which uses the common neighbor algorithm to maintain the local network structure. ...

Reference:

Pairwisely constrained symmetric non-negative matrix factorization for link prediction in complex networks using degree-related clustering coefficient and improved closeness centrality