Vertaile menetelmiä

	Tietograafiembeddingit	Graafineuraaliverkko	Word2Vec
Tieteenala	Verkostoanalyysi	Verkostoanalyysi	Tekstinlouhinta
Menetelmäperhe	Machine learning	Process / pipeline	Process / pipeline
Syntyvuosi	2013	2017–2018 (major variants)	2013
Kehittäjä	Bordes, Usunier, García-Durán, Weston & Yakhnenko	—	Tomas Mikolov et al.
Tyyppi	Graph representation learning via low-dimensional vector embeddings	Deep learning on graph-structured data	Neural word-embedding model
Alkuperäislähde	Bordes, A., Usunier, N., García-Durán, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 26.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Rinnakkaisnimet	KG Embeddings, Knowledge Graph Representation Learning, Relational Embeddings, Bilgi Grafı Gömme	GNN, GCN, GAT, GraphSAGE	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Liittyvät	3	5	4
Tiivistelmä	Knowledge Graph Embeddings (KGE) are a family of methods that represent entities and relations in a knowledge graph as dense, low-dimensional vectors in a continuous space. The foundational model, TransE, was introduced by Bordes, Usunier, García-Durán, Weston, and Yakhnenko in 2013. TransE treats each relation as a translation in embedding space — the head entity vector plus the relation vector should approximate the tail entity vector for any true triple (h, r, t). This simple geometric principle enabled effective link prediction and knowledge base completion at scale.	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.	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.