Node Representation Learning

Graph representation learning with node2vec - Keras

node2vec is a simple, yet scalable and effective technique for learning low-dimensional embeddings for nodes in a graph by optimizing a neighborhood-preserving ...

"Neural Representation Learning for Semi-Supervised Node ...

Recently, advances in representation learning for graph data have made great strides for the semi-supervised node classification. However, most of the methods ...

Graph Representation Learning and Its Applications: A Survey - MDPI

Early graph transformer models aim to learn tree-like graphs, which mainly aim at learning node embeddings in tree-like graphs where nodes are arranged ...

Simple Unsupervised Graph Representation Learning - AAAI

UGRL does not require abundant labeled nodes for training, it can output discriminative representation by simultaneously learning representations and preserving ...

Self-Supervised Node Representation Learning via Node-to ...

Self-supervised node representation learning aims to learn node representations from unlabelled graphs that rival the supervised counterparts.

Node embeddings for Beginners - Towards Data Science

The result of such representation learning is a node embedding (and so does graph embedding, edge embedding exist). So I will use the terms ...

(PDF) Unsupervised Graph Representation Learning with Inductive ...

PDF | Network science has witnessed a surge in popularity, driven by the transformative power of node representation learning for diverse applications.

Out-of-sample Node Representation Learning for Heterogeneous ...

To solve this problem, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of- sample nodes ...

Inductive Representation Learning on Large Graphs

Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information ...

Graph Representation Learning - Jian Tang

In this course, I will introduce the latest progress on learning representations of graphs such as node representation learning, graph visualization, knowledge ...

Fair Node Representation Learning via Adaptive Data Augmentation

This paper develops fairness-aware graph data augmentation schemes based on an analysis for the reduction of implicit bias in node representations.

Graph Neural Networks with Information Anchors for Node ...

Graph Neural Network (GNN) based node representation learning is an emerging learning paradigm that embeds network nodes into a low dimensional vector space ...

Subset Node Representation Learning over Large Dynamic Graphs

Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge ...

Unsupervised Structural Graph Node Representation Learning

Unsupervised Graph Representation Learning methods learn a numerical representation of the nodes in a graph. The generated representations ...

Graph Representation Learning Book - McGill University

Graph Representation Learning Book. William L. Hamilton, McGill University ... Updated September 2020.] Part I: Node Embeddings. Chapter 3: Neighborhood ...

Node Representation Learning in Higher-order Networks

In this work, we propose a node representation learning frame- work called EVO or Embedding Variable Orders, which captures non-Markovian dependencies by ...

vGraph: A Generative Model for Joint Community Detection and ...

... graph Laplacian. Another important task of graph analysis is node representation learning, where nodes are described using low-dimensional features. Node ...

Innovations in Graph Representation Learning - Google Research

The first paper introduces a novel technique to learn multiple embeddings per node, enabling a better characterization of networks with overlapping communities.

The overall architecture of node representation learning. We take the...

Download scientific diagram | The overall architecture of node representation learning. We take the center of a window as our target node.

[D] Masked Node Classification/Representation Learning for Graph ...

I am looking for papers that deal with masked node classification/representation learning for graph neural networks.