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:2608.23802v1 Announce Type: cross Abstract: Many common data dependencies can be characterized by graphs: time series data are sequential (chain graph), images appear as pixels (lattice graph),...
arXiv:2606. 07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion.
The paper introduces TranCE, a doubly‑robust algorithm for estimating causal effects when an intervention is applied to one network but the interest lies in another, differing network. By extending selection diagrams to capture covariate and structural network shifts, the authors derive transport formulas for direct, spillover, and total effects, and validate the method on semi‑synthetic social‑network benchmarks and a real weather‑insurance field experiment.
The paper introduces JSP-GFN, a Generative Flow Network that jointly infers the structure and parameters of a Bayesian Network. It sequentially generates a directed acyclic graph edge by edge and then samples the corresponding conditional probability parameters once the full structure is known. Experiments on simulated and real data show that JSP‑GFN accurately approximates the joint posterior and outperforms existing methods.
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.
The paper develops a nonasymptotic theoretical framework for estimating hyperparameters in hierarchical Bayesian models applied to large, inhomogeneous complex network dynamical systems. It provides bounds on the deviation of hyperparameter estimates as network size grows, first for independent nodes and then extending to weakly‑dependent nodes, and validates these results with numerical experiments on SIS and spiking neuronal network models.
arXiv:2509.13267v3 Announce Type: replace-cross Abstract: A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling in...
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.