Inductive Learning
65 papers with code • 0 benchmarks • 0 datasets
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Deep Graph Infomax
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
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many real-world applications.
Deep Graph Contrastive Representation Learning
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
Random walks are at the heart of many existing network embedding methods.
Neural Logic Machines
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
From social networks to protein complexes to disease genomes to visual data, hypergraphs are everywhere.
Is deep learning necessary for simple classification tasks?
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
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
In this paper, we propose the Graph Temporal Edge Aggregation (GTEA) framework for inductive learning on Temporal Interaction Graphs (TIGs).