Semanlink - Jure Leskovec

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Description Page Jure Leskovec

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5 Documents (Long List)

Stanford CS224W GraphML Tutorials – Medium

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2023-05-18 About

[2210.09338] Deep Bidirectional Language-Knowledge Graph Pretraining

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2022-10-23 About

[1607.00653] node2vec: Scalable Feature Learning for Networks

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2020-08-08 About

Bringing traditional ML to your Neo4j Graph with node2vec | Dave Voutila

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2020-08-06 About

TUTORIAL: Representation Learning on Networks - TheWebConf 2018

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Network representation learning (NRL): Approaches that automatically learn to encode network structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction

**Goal of representation learning for networks: efficient task-independant feature learning for ML in networks.** But it's hard. DL toolbox are designed for single sequences or grids (for instance CNN for images, RNN or word2vec are fixed size), but networks are far more complex!

from the abstract:

> In this tutorial, we will cover key advancements in NRL over the last decade, with an emphasis on fundamental advancements made in the last two years. We will discuss classic matrix factorization-based methods (e.g., Laplacian eigenmaps), random-walk based algorithms (e.g., DeepWalk and node2vec), as well as very recent advancements in graph convolutional networks (GCNs). We will cover methods to embed individual nodes (see [node embeddings](/tag/node_embeddings)) as well as approaches to embed entire (sub)graphs, and in doing so, we will present a unified framework for NRL.

2018-05-05 About

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sl:creationDate : 2018-05-10
sl:creationTime : 2018-05-10T14:28:27Z
sl:describedBy : https://cs.stanford.edu/%7Ejure/
rdf:type : sl:Tag
skos:prefLabel : Jure Leskovec