15 citations · 59 across the 19 of their papers we have counts for
19 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…
TiGer: Self-Supervised Purification for Time-evolving Graphs
Hyeonsoo Jo, Jongha Lee, Fanchen Bu +1
Time-evolving graphs, such as social and citation networks, often contain noise that distorts structural and temporal patterns, adversely affecting downstream tasks, such as node c…
DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation
Junghun Lee, Hyunju Kim, Fanchen Bu +2
In social networks, people influence each other through social links, which can be represented as propagation among nodes in graphs. Influence minimization (IMIN) is the problem of…
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…
Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms
Fanchen Bu, Ruochen Yang, Paul Bogdan +1
Desirable random graph models (RGMs) should (i) reproduce common patterns in real-world graphs (e.g., power-law degrees, small diameters, and high clustering), (ii) generate variab…
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…
A Survey on Hypergraph Mining: Patterns, Tools, and Generators
Geon Lee, Fanchen Bu, Tina Eliassi-Rad +1
Hypergraphs, which belong to the family of higher-order networks, are a natural and powerful choice for modeling group interactions in the real world. For example, when modeling co…
Robust Graph Clustering via Meta Weighting for Noisy Graphs
Hyeonsoo Jo, Fanchen Bu, Kijung Shin
How can we find meaningful clusters in a graph robustly against noise edges? Graph clustering (i.e., dividing nodes into groups of similar ones) is a fundamental problem in graph a…
On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithms
Fanchen Bu, Kijung Shin
Graphs are a powerful mathematical model, and they are used to represent real-world structures in various fields. In many applications, real-world structures with high connectivity…
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…
How Transitive Are Real-World Group Interactions? -- Measurement and Reproduction
Sunwoo Kim, Fanchen Bu, Minyoung Choe +2
Many real-world interactions (e.g., researcher collaborations and email communication) occur among multiple entities. These group interactions are naturally modeled as hypergraphs.…
Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm
Fanchen Bu, Shinhwan Kang, Kijung Shin
What are the relations between the edge weights and the topology in real-world graphs? Given only the topology of a graph, how can we assign realistic weights to its edges based on…
Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes
Hyunju Kim, Jihoon Ko, Fanchen Bu +1
Simplicial complexes are higher-order combinatorial structures which have been used to represent real-world complex systems. In this paper, we concentrate on the local patterns in…
Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications
Fanchen Bu, Geon Lee, Kijung Shin
Hypergraphs are a powerful abstraction for modeling high-order relations, which are ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (i.e., subsets of nodes…
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs
Fanchen Bu, Dong Eui Chang
The optimization with orthogonality has been shown useful in training deep neural networks (DNNs). To impose orthogonality on DNNs, both computational efficiency and stability are…
Double Prioritized State Recycled Experience Replay
Fanchen Bu, Dong Eui Chang
Experience replay enables online reinforcement learning agents to store and reuse the previous experiences of interacting with the environment. In the original method, the experien…