# Inductive Representation Learning on Large Graphs

@inproceedings{Hamilton2017InductiveRL, title={Inductive Representation Learning on Large Graphs}, author={William L. Hamilton and Zhitao Ying and Jure Leskovec}, booktitle={NIPS}, year={2017} }

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. [...] Key Method Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving… Expand

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Inductive Representation Learning on Large Graphs

#### 4,257 Citations

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

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Graph2Gauss is proposed - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification and the benefits of modeling uncertainty are demonstrated. Expand

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Midterm Report

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This paper presents a context-aware unsupervised dual encoding framework, CADE, to generate representations of nodes by combining real-time neighborhoods with neighbor-attentioned representation, and preserving extra memory of known nodes. Expand

Graph Auto-Encoders for Learning Edge Representations

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This paper proposes a new model (in the form of an auto-encoder) to learn edge embeddings in (un)directed graphs and empirically evaluates the approach in two different tasks, namely edge classification and link prediction. Expand

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