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Inductive Representation Learning on Large Graphs
TLDR
We present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Expand
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Hierarchical Graph Representation Learning with Differentiable Pooling
TLDR
We propose a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. Expand
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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
TLDR
We develop a robust methodology for quantifying semantic change by evaluating word embeddings (PPMI, SVD, word2vec) against known historical changes. Expand
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Representation Learning on Graphs: Methods and Applications
TLDR
We provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. Expand
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Deep Graph Infomax
TLDR
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner, which is readily applicable to both transductive and inductive learning setups. Expand
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Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TLDR
We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Expand
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GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
TLDR
GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated. Expand
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Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
TLDR
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Expand
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Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
TLDR
We show how two different distributional measures can be used to detect two different types of semantic change in a word's distributional semantics. Expand
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Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
TLDR
We combine domain-specific word embeddings with a label propagation framework to induce accurate domain- specific sentiment lexicons using small sets of seed words. Expand
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