arXiv:2211. 15590v2 Announce Type: replace Abstract: Analyzing the behavior of complex interdependent networks requires complete information about the network topology and the interdependent links across networks.
arXiv:2606. 07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion.
arXiv:2607. 20480v1 Announce Type: new Abstract: Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reliable connectivity records remains challenging in practice due to heterogeneous and imperfect utility data.
arXiv:2607. 08918v1 Announce Type: new Abstract: Cyber-physical power systems are vulnerable to cascading failures caused by tight interdependencies between power and communication infrastructures.
arXiv:2607. 04650v1 Announce Type: cross Abstract: Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size.
arXiv:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.
arXiv:2606. 05636v1 Announce Type: new Abstract: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare.
Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size. We propose a decomposition framework based on directed convex subgraphs and introduce a minimal d-decomposition tree.
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
arXiv:2606. 10782v1 Announce Type: cross Abstract: The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and availability of advanced features.
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
arXiv:2607. 23197v1 Announce Type: new Abstract: Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations from normal behavior can indicate process disruption.
arXiv:2608. 06772v1 Announce Type: new Abstract: Accurate estimation of building energy use is essential for achieving carbon neutral and sustainable buildings.