9 papers
Automating and Scaling Behavioral Scientific Research on AI Agents
Soo Yong Lee, Jongha Lee, Jaewan Chun +7
As AI agents are increasingly deployed in complex environments, understanding their behaviors becomes critical. Yet behavioral scientific research on AI agents remains manual and l…
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…
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…
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…
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…