Inductive Learning

65 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Deep Graph Infomax

PetarV-/DGI ICLR 2019

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.

GraphSAINT: Graph Sampling Based Inductive Learning Method

GraphSAINT/GraphSAINT ICLR 2020

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.

Representation Learning for Attributed Multiplex Heterogeneous Network

cenyk1230/GATNE 5 May 2019

Network embedding (or graph embedding) has been widely used in many real-world applications.

Deep Graph Contrastive Representation Learning

CRIPAC-DIG/GRACE 7 Jun 2020

Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.

Learning Role-based Graph Embeddings

benedekrozemberczki/karateclub IJCAI 2018

Random walks are at the heart of many existing network embedding methods.

Neural Logic Machines

google/neural-logic-machines ICLR 2019

We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning.

Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs

Josh-Payne/deep-hyperedges 7 Oct 2019

From social networks to protein complexes to disease genomes to visual data, hypergraphs are everywhere.

Is deep learning necessary for simple classification tasks?

EpistasisLab/tpot 11 Jun 2020

providing a head-to-head comparison of AutoML and DL in the context of binary classification on 6 well-characterized public datasets, and (2.)

Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

hugochan/IDGL NeurIPS 2020

In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedding.

GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation

xslangley/gtea 11 Sep 2020

In this paper, we propose the Graph Temporal Edge Aggregation (GTEA) framework for inductive learning on Temporal Interaction Graphs (TIGs).