Node Representation Learning

We study several methods to represent a graph in the embedding space. By “embedding” we mean mapping each node in a network into a low-dimensional space.

Node representation learning with GraphSAGE and ... - StellarGraph

This notebook is a short demo of how Stellargraph Unsupervised GraphSAGE can be used to learn embeddings of the nodes representing papers in the CORA citation ...

[2302.04626] Self-Supervised Node Representation Learning via ...

In this work, we present simple-yet-effective self-supervised node representation learning via aligning the hidden representations of nodes and their ...

Dynamic network link prediction with node representation learning ...

Dynamic network link prediction is extensively applicable in various scenarios, and it has progressively emerged as a focal point in data ...

[2203.12265] Node Representation Learning in Graph via ... - arXiv

We present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden ...

Property graph representation learning for node classification

We present a new framework called Enhanced Property Graph Embedding (EPGE)—a graph representation learning framework to address above-mentioned ...

Self-Supervised Node Representation Learning via ... - IEEE Xplore

The key towards learning informative node representations lies in how to effectively gain contextual information from the graph structure. In this work, we ...

Node representation learning with graph augmentation for ...

We propose a novel sequential recommendation model, called GNSR, which utilizes graph structures at various levels to enhance the node representation.

Unsupervised representation learning - StellarGraph - Read the Docs

StellarGraph provides numerous algorithms for doing unsupervised node, edge and graph representation learning on graphs. This folder contains demos of all of ...

Representation Learning on Networks

These network representation learning (NRL) approaches remove the need for ... node classification, node clustering, and link prediction. In this ...

SEESAW: Do Graph Neural Networks Improve Node Representation...

However, in tandem with this transition, an imperative question arises: do GNNs always outperform shallow embedding methods in node representation learning?

Demystifying and Mitigating Bias for Node Representation Learning

This work theoretically explains the sources of bias in node representations obtained via graph neural networks (GNNs).

Subset Node Representation Learning over Large Dynamic Graphs

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

Foundations of Node Representation Learning - UMass ScholarWorks

Low-dimensional node representations, also called node embeddings, are a cornerstone in the modeling and analysis of complex networks.

7.Graph Representation Learning - Wandb

Figuring out embeddings of nodes that represent node similarity within the networks. Made by Anil using Weights & Biases.

Roy-lab/graph-representation-learning: Comparison of various node ...

This file takes a filtered weighted graph in edgelist format and transforms it into an adjacency matrix format. The Output file can be used by the spectral ...

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 ...

Node Representation Learning for Directed Graphs

Cite this ... Khosla, Megha ; Leonhardt, Jurek ; Nejdl, Wolfgang ; Anand, Avishek. / Node Representation Learning for Directed Graphs. MACHINE LEARNING AND ...

Graph Representation Learning - an overview | ScienceDirect Topics

The two presented methods for graph representation learning: ( a ) Node embeddings and ( b ) Graph Neural Networks. ( a ) Nodes are mapped to a low dimensional ...

Fair Benchmark for Unsupervised Node Representation Learning

Most machine-learning algorithms assume that instances are independent of each other. This does not hold for networked data. Node representation learning ...