Bandingkan kaedah

	Pengesanan Komuniti	Graph Neural Network	Analisis Rangkaian Berlapis
Bidang	Analisis Rangkaian	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	2002–2019 (algorithm family)	2017–2018 (major variants)	2013–2014 (formal mathematical framework)
Pengasas	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	—	Kivelä et al. (2014); De Domenico et al. (2013)
Jenis	Graph-partitioning / clustering algorithm family	Deep learning on graph-structured data	Graph-theoretic network model
Sumber perintis	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.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271.
Alias	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	GNN, GCN, GAT, GraphSAGE	multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks)
Berkaitan	5	5	6
Ringkasan	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?	A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.	Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges.