6 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption
Sunwoo Kim, Soo Yong Lee, Kyungho Kim +3
Unsupervised node representation learning aims to obtain meaningful node embeddings without relying on node labels. To achieve this, graph convolution, which aggregates information…
View Space: Learning Representation across Arbitrary Graphs
Dooho Lee, Myeong Kong, Minho Jeong +1
Generalizing pretrained models to unseen datasets without retraining is a central challenge toward foundation models. Achieving fully inductive inference on numerical data is parti…
ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation
Sunwoo Kim, Geon Lee, Kyungho Kim +2
Recently, large language models (LLMs) have been widely used as recommender systems, owing to their reasoning capability and effectiveness in handling cold-start items. A common ap…
'Hello, World!': Making GNNs Talk with LLMs
Sunwoo Kim, Soo Yong Lee, Jaemin Yoo +1
While graph neural networks (GNNs) have shown remarkable performance across diverse graph-related tasks, their high-dimensional hidden representations render them black boxes. In t…
Emergence of psychopathological computations in large language models
Soo Yong Lee, Hyunjin Hwang, Taekwan Kim +5
Can large language models (LLMs) instantiate computations of psychopathology? In this work, we establish a computational-theoretical framework to provide an account of psychopathol…