• Corpus ID: 173187964

# End to end learning and optimization on graphs

@article{Wilder2019EndTE,
title={End to end learning and optimization on graphs},
author={Bryan Wilder and Eric Ewing and Bistra N. Dilkina and Milind Tambe},
journal={ArXiv},
year={2019},
volume={abs/1905.13732}
}
• Published 31 May 2019
• Computer Science
• ArXiv
Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and…

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## References

SHOWING 1-10 OF 72 REFERENCES
Learning Combinatorial Optimization Algorithms over Graphs
• Computer Science
NIPS
• 2017
This paper proposes a unique combination of reinforcement learning and graph embedding that behaves like a meta-algorithm that incrementally constructs a solution, and the action is determined by the output of agraph embedding network capturing the current state of the solution.
GAP: Generalizable Approximate Graph Partitioning Framework
• Computer Science
ArXiv
• 2019
This work proposes GAP, a Generalizable Approximate Partitioning framework that takes a deep learning approach to graph partitioning, and defines a differentiable loss function that represents the partitioning objective and use backpropagation to optimize the network parameters.
Hierarchical Graph Representation Learning with Differentiable Pooling
• Computer Science
NeurIPS
• 2018
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.
Attention, Learn to Solve Routing Problems!
• Computer Science
ICLR
• 2019
A model based on attention layers with benefits over the Pointer Network is proposed and it is shown how to train this model using REINFORCE with a simple baseline based on a deterministic greedy rollout, which is more efficient than using a value function.
Learning Role-based Graph Embeddings
• Computer Science, Mathematics
ArXiv
• 2018
The Role2Vec framework is introduced, which uses the flexible notion of attributed random walks, and serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks.
node2vec: Scalable Feature Learning for Networks
• Computer Science
KDD
• 2016
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.
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
• Computer Science
AAAI
• 2019
This work focuses on combinatorial optimization problems and introduces a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions, and shows that decisionfocused learning often leads to improved optimization performance compared to traditional methods.
Inductive Representation Learning on Large Graphs
• Computer Science
NIPS
• 2017
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.
Learning Deep Representations for Graph Clustering
• Computer Science
AAAI
• 2014
This work proposes a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs $k$-means algorithm on the embedding to obtain the clustering result, which significantly outperforms conventional spectral clustering.
Stochastic Submodular Maximization: The Case of Coverage Functions
• Computer Science
NIPS
• 2017
This model captures situations where the discrete objective arises as an empirical risk, or is given as an explicit stochastic model, and yields solutions that are guaranteed to match the optimal approximation guarantees, while reducing the computational cost by several orders of magnitude, as demonstrated empirically.