So sánh phương pháp

	Phát hiện Cộng đồng	Nhúng mạng
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2002–2019 (algorithm family)	2014 (DeepWalk); 2016 (Node2Vec)
Người khởi xướng	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	—
Loại	Graph-partitioning / clustering algorithm family	Representation learning / unsupervised network method
Công trình gốc	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.	Grover, A. & Leskovec, J. (2016). Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 855-864.
Tên gọi khác	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	node embedding, graph embedding, network representation learning, Ağ Gömme (Node2Vec, DeepWalk, LINE)
Liên quan	5	3
Tóm tắt	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?	Network embedding is a family of representation-learning methods that map each node of a graph into a dense, low-dimensional vector while preserving the network's structural properties. The approach was formalised for social-network data by Perozzi, Al-Rfou, and Skiena with DeepWalk (2014), which adapted the Word2Vec skip-gram model to random walks on graphs, and extended by Grover and Leskovec with Node2Vec (2016), which introduced a biased random walk that balances breadth-first and depth-first exploration. These embeddings turn relational data into feature vectors that standard machine-learning classifiers and clustering algorithms can consume directly.