1 citations · 2 across the 7 of their papers we have counts for
7 papers
Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling
Chaoyu Zhang, Hexuan Yu, Heng Jin +6
Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these systems often originate from a s…
LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search
Nikhil Abhyankar, Sha Li, Sanchit Kabra +3
Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains. Existing approaches cast disco…
Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks
Roshni Anna Jacob, Prithvi Poddar, Jaidev Goel +3
Extreme weather events and cyberattacks can cause component failures and disrupt the operation of power distribution networks (DNs), during which reconfiguration and load shedding…
Community detection robustness of graph neural networks
Jaidev Goel, Pablo Moriano, Ramakrishnan Kannan +1
Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message pass…
TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction
Hao Li, Hao Wan, Yuzhou Chen +3
Dynamic graphs evolve continuously, presenting challenges for traditional graph learning due to their changing structures and temporal dependencies. Recent advancements have shown…
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
Naheed Anjum Arafat, Debabrota Basu, Yulia Gel +1
Capitalizing on the intuitive premise that shape characteristics are more robust to perturbations, we bridge adversarial graph learning with the emerging tools from computational t…
TopoGCL: Topological Graph Contrastive Learning
Yuzhou Chen, Jose Frias, Yulia R. Gel
Graph contrastive learning (GCL) has recently emerged as a new concept which allows for capitalizing on the strengths of graph neural networks (GNNs) to learn rich representations…