56 citations · 68 across the 11 of their papers we have counts for
11 papers
Causal Behavioral Evaluation of AI Agents at Scale via Automated Behavioral Science
Soo Yong Lee, Jongha Lee, Jaewan Chun +8
As AI agents are increasingly deployed in complex and new environments, knowing the conditions that influence their behavior becomes an indispensable step for their reliable and sa…
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…
'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…
On Measuring Unnoticeability of Graph Adversarial Attacks: Observations, New Measure, and Applications
Hyeonsoo Jo, Hyunjin Hwang, Fanchen Bu +3
Adversarial attacks are allegedly unnoticeable. Prior studies have designed attack noticeability measures on graphs, primarily using statistical tests to compare the topology of or…
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
Sunwoo Kim, Soo Yong Lee, Fanchen Bu +4
Graph autoencoders (Graph-AEs) learn representations of given graphs by aiming to accurately reconstruct them. A notable application of Graph-AEs is graph-level anomaly detection (…
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
Fanchen Bu, Hyeonsoo Jo, Soo Yong Lee +2
Combinatorial optimization (CO) is naturally discrete, making machine learning based on differentiable optimization inapplicable. Karalias & Loukas (2020) adapted the probabilistic…
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Sunwoo Kim, Soo Yong Lee, Yue Gao +3
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the…
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Sunwoo Kim, Shinhwan Kang, Fanchen Bu +3
Hypergraphs are marked by complex topology, expressing higher-order interactions among multiple nodes with hyperedges, and better capturing the topology is essential for effective…
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Soo Yong Lee, Sunwoo Kim, Fanchen Bu +3
How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between…
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
Soo Yong Lee, Fanchen Bu, Jaemin Yoo +1
Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation. Attent…