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	ग्राफ न्यूरल नेटवर्क	पेज-रैंक सेंट्रैलिटी	Word2Vec
क्षेत्र	नेटवर्क विश्लेषण	नेटवर्क विश्लेषण	पाठ खनन
परिवार	Process / pipeline	Machine learning	Process / pipeline
उद्भव वर्ष	2017–2018 (major variants)	1999	2013
प्रवर्तक	—	Page, Brin, Motwani & Winograd	Tomas Mikolov et al.
प्रकार	Deep learning on graph-structured data	Iterative link-based centrality algorithm	Neural word-embedding model
मौलिक स्रोत	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
उपनाम	GNN, GCN, GAT, GraphSAGE	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
संबंधित	5	2	4
सारांश	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.	PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.