Symmetric Nonnegative Matrix Factorization for Graph Clustering

By Da Kuang, Haesun Park, Chris H. Q. Ding. SDM, 2012

482 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f7b78a28a901a63161e4291310d29ca0176da315

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huan Liu
2. Xia Hu
3. Jundong Li
4. Jiliang Tang
5. Xiao Huang
6. 刘晗
7. Leman Akoglu
8. Danai Koutra

In Computer Vision and Pattern Recognition

1. 刘晗
2. Philip S. Yu
3. Jiebo Luo
4. Jiliang Tang
5. Jundong Li
6. Dacheng Tao
7. Xiaotong Zhang
8. Xianchao Zhang

In Statistical and Nonlinear Physics

1. Huan Liu
2. Xia Hu
3. Jundong Li
4. Xiao Huang
5. Fred Morstatter
6. Danai Koutra
7. Jiliang Tang
8. Shirui Pan

Citations in the corpus

• Multi-view clustering: A survey (2018, unknown): “Based on SymNMF[151], Zhang et al.[152] introduced a graph regularized symmetric NMF framework for MvC.”
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning (2021, methodology): “Basic SymNMF (2012) [74] Develop a symmetric NMF framework based on Newton-like for graph clustering.”
• Accelerated Attributed Network Embedding (2017, methodology): “Motivated by the symmetric matrix factorization [17], we propose to approximate attribute affinity matrix S with the product of H and H>.”
• Feature Selection (2016, background): “NetFS first uncovers a low-rank latent representation U by a symmetric NMF model (Gegick, 2012).”
• Multi-Task Clustering with Model Relation Learning (2018, methodology): “We compare MTCMRL with the single-task clustering meth-ods k -means, symmetric nonnegative matrix factorization (SNMF) [Kuang et al. , 2012], constrained Laplacian rank graph-based clustering (CLR) [Nie et al. , 2016] and symmetric nonnegative matrix factorization with linear regression model…”
• Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, methodology): “Here we follow the assumption in [Kuang et al. , 2012] to see k i as the cluster number of data objects within the i th view, and the data-cluster symmetric matrix factorization has been widely adopted by the numerous existing research including semi-supervised learning [Wang et al. , 2014c; Wu et…”
• Training Neural Networks with Local Error Signals (2019, methodology): “) in the first term of (2), this minimization can perform different graph clustering objectives, namely ratio association, kernel clustering and normalized cuts (Kuang et al., 2012).”
• Graph based anomaly detection and description: a survey (2014, methodology): “2011; Nikulin and Huang 2012) to (graph) clustering (Kuang et al. 2012; Wang et al. 2012b).”
• Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework (2013, methodology): “In the data mining community, variants of NMF such as 988 convex and semi-NMFs [25,75], orthogonal tri-NMF [26], and group-sparse NMF [56] have 989 been proposed, and using NMF for clustering has been shown to be successful [12,57,63].”