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node2vec: Scalable Feature Learning for Networks
TLDR
In node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks, a flexible notion of a node's network neighborhood is defined and a biased random walk procedure is designed, which efficiently explores diverse neighborhoods. Expand
Inductive Representation Learning on Large Graphs
TLDR
GraphSAGE is presented, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data and outperforms strong baselines on three inductive node-classification benchmarks. Expand
How Powerful are Graph Neural Networks?
TLDR
This work characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures, and develops a simple architecture that is provably the most expressive among the class of GNNs. Expand
{SNAP Datasets}: {Stanford} Large Network Dataset Collection
A collection of more than 50 large network datasets from tens of thousands of nodes and edges to tens of millions of nodes and edges. In includes social networks, web graphs, road networks, internetExpand
Friendship and mobility: user movement in location-based social networks
TLDR
A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance. Expand
Cost-effective outbreak detection in networks
TLDR
This work exploits submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm and achieving speedups and savings in storage of several orders of magnitude. Expand
Hidden factors and hidden topics: understanding rating dimensions with review text
TLDR
This paper aims to combine latent rating dimensions (such as those of latent-factor recommender systems) with latent review topics ( such as those learned by topic models like LDA), which more accurately predicts product ratings by harnessing the information present in review text. Expand
Graph evolution: Densification and shrinking diameters
TLDR
A new graph generator is provided, based on a forest fire spreading process that has a simple, intuitive justification, requires very few parameters, and produces graphs exhibiting the full range of properties observed both in prior work and in the present study. Expand
Graphs over time: densification laws, shrinking diameters and possible explanations
TLDR
A new graph generator is provided, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study. Expand
Hierarchical Graph Representation Learning with Differentiable Pooling
TLDR
DiffPool is proposed, 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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