Extrapolating paths with graph neural networks

@article{Cordonnier2019ExtrapolatingPW,
  title={Extrapolating paths with graph neural networks},
  author={Jean-Baptiste Cordonnier and Andreas Loukas},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.07518}
}
We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. In particular, we focus on natural paths occurring as a by-product of the interaction of an agent with a network---a driver on the transportation network, an information seeker in Wikipedia, or a client in an online shop. Our interest is sparked by the realization that, in contrast to shortest-path problems, natural… Expand
3 Citations
Principled Simplicial Neural Networks for Trajectory Prediction
TLDR
A simple convolutional architecture is proposed, rooted in tools from algebraic topology, for the problem of trajectory prediction, and it is shown that it obeys all three of these properties when an odd, nonlinear activation function is used. Expand
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
TLDR
This work uses a neural network to parametrize a probability distribution over sets and shows that when the network is optimized w.r.t. a suitably chosen loss, the learned distribution contains, with controlled probability, a low-cost integral solution that obeys the constraints of the combinatorial problem. Expand
Graph Neural Networks: Taxonomy, Advances and Trends
TLDR
A novel taxonomy for the graph neural networks is provided, and up to 400 relevant literatures are referred to to show the panorama of the graph Neural networks. Expand

References

SHOWING 1-10 OF 27 REFERENCES
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
Human wayfinding in information networks
TLDR
A large-scale study of human wayfinding, in which, given a network of links between the concepts of Wikipedia, people play a game of finding a short path from a given start to a given target concept by following hyperlinks. Expand
Relational inductive biases, deep learning, and graph networks
TLDR
It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Expand
DeepWalk: online learning of social representations
TLDR
DeepWalk is an online learning algorithm which builds useful incremental results, and is trivially parallelizable, which make it suitable for a broad class of real world applications such as network classification, and anomaly detection. Expand
Semi-Supervised Classification with Graph Convolutional Networks
TLDR
A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin. Expand
Modeling Trajectories with Recurrent Neural Networks
TLDR
Two RNN-based models are designed which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to address the constraints of topological structure on trajectory modeling. Expand
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
TLDR
This work presents a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Expand
Long Short-Term Memory
TLDR
A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Expand
...
1
2
3
...