قارن الطرق

	النوى الرسومية (Graph Kernels)	Knowledge Graph Embeddings
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	2010	2013
صاحب الطريقة	Vishwanathan, Schraudolph, Kondor & Borgwardt	Bordes, Usunier, García-Durán, Weston & Yakhnenko
النوع	Positive semi-definite kernel function over graphs	Graph representation learning via low-dimensional vector embeddings
المصدر التأسيسي	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.
الأسماء البديلة	Structured Graph Kernels, Kernel Methods on Graphs, Graf Çekirdekleri, Graph Similarity Kernels	KG Embeddings, Knowledge Graph Representation Learning, Relational Embeddings, Bilgi Grafı Gömme
ذات صلة	2	3
الملخص	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.