পদ্ধতির তুলনা করুন

	গ্রাফ নিউরাল নেটওয়ার্ক	কমিউনিটি ডিটেকশন	মাল্টিলেয়ার নেটওয়ার্ক বিশ্লেষণ
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2017–2018 (major variants)	2002–2019 (algorithm family)	2013–2014 (formal mathematical framework)
প্রবর্তক	—	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)
ধরন	Deep learning on graph-structured data	Graph-partitioning / clustering algorithm family	Graph-theoretic network model
মৌলিক উৎস	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	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.	Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271.
অপর নাম	GNN, GCN, GAT, GraphSAGE	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks)
সম্পর্কিত	5	5	6
সারসংক্ষেপ	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.	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?	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.