Publications

[C3] Models and Benchmarks for Representation Learning of Partially Observed Subgraphs

Dongkwan Kim, Jiho Jin, Jaimeen Ahn and Alice Oh. International Conference on Information and Knowledge Management (CIKM, Short Papers Track), 2022

Paper arXiv Code

[C2] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision

Dongkwan Kim and Alice Oh. International Conference on Learning Representations (ICLR), 2021

One-sentence Summary: We propose a method that self-supervise graph attention through edges and it should be designed according to the average degree and homophily of graphs.

Paper arXiv Code Slide

[C1] Homogeneity-Based Transmissive Process To Model True and False News in Social Networks

Jooyeon Kim, Dongkwan Kim, and Alice Oh. International Conference on Web Search and Data Mining (WSDM), 2019

Paper arXiv Code Data

[P1] Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning

Dongkwan Kim and Alice Oh. Arxiv, 2024

One-sentence Summary: We propose Subgraph-To-Node translation to effectively and efficiently learn representations of subgraphs by coarsely translating subgraphs into nodes.

arXiv

[W3] Efficient Representation Learning of Subgraphs by Subgraph-To-Node Translation

Dongkwan Kim and Alice Oh. Workshop on Geometrical and Topological Representation Learning at ICLR (ICLR GTRL), 2022

One-sentence Summary: We propose Subgraph-To-Node translation to efficiently learn representations of subgraphs by coarsely translating subgraphs into nodes.

Paper arXiv

[W2] Supervised Graph Attention Network for Semi-Supervised Node Classification

Dongkwan Kim and Alice Oh. Workshop on Graph Representation Learning at NeurIPS (NeurIPS GRL), 2019

Note: The full version is published at ICLR 2021.

Paper

[W1] Additive Compositionality of Word Vectors

Yeon Seonwoo, Sungjoon Park, Dongkwan Kim, and Alice Oh. Workshop on Noisy User-generated Text at EMNLP (EMNLP W-NUT), 2019

Paper