Salīdzināt metodes

	Grafu kodoli	Knowledge Graph Embeddings
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010	2013
Autors	Vishwanathan, Schraudolph, Kondor & Borgwardt	Bordes, Usunier, García-Durán, Weston & Yakhnenko
Tips	Positive semi-definite kernel function over graphs	Graph representation learning via low-dimensional vector embeddings
Pirmavots	Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11, 1201–1242.	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.
Citi nosaukumi	Structured Graph Kernels, Kernel Methods on Graphs, Graf Çekirdekleri, Graph Similarity Kernels	KG Embeddings, Knowledge Graph Representation Learning, Relational Embeddings, Bilgi Grafı Gömme
Saistītās	2	3
Kopsavilkums	Graph kernels are positive semi-definite kernel functions that measure the similarity between two graphs by comparing their shared substructures — such as random walks, shortest paths, or subtree patterns. Introduced in a unified framework by Vishwanathan, Schraudolph, Kondor, and Borgwardt (2010), they bridge kernel methods and graph-structured data, enabling algorithms like SVMs to operate directly on graphs without requiring an explicit vectorization step.	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.