Search Results for author: Raehyun Kim

Found 7 papers, 6 papers with code

Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs

1 code implementation11 Jun 2021 Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, Sean S. Yi, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim

Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs.

Node Classification

MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System

no code implementations10 Jul 2020 Jinho Lee, Raehyun Kim, Seok-Won Yi, Jaewoo Kang

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest.

Management Multi-agent Reinforcement Learning +1

Graph Transformer Networks

1 code implementation NeurIPS 2019 Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim

In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion.

General Classification Link Prediction +2

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

3 code implementations7 Aug 2019 Raehyun Kim, Chan Ho So, Minbyul Jeong, Sang-Hoon Lee, Jinkyu Kim, Jaewoo Kang

Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.

Graph Attention Graph Classification +2

SAIN: Self-Attentive Integration Network for Recommendation

1 code implementation27 May 2019 Seoungjun Yun, Raehyun Kim, Miyoung Ko, Jaewoo Kang

To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed.

Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network

1 code implementation28 Feb 2019 Jinho Lee, Raehyun Kim, Yookyung Koh, Jaewoo Kang

Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries.

Stock Market Prediction

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