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Long power outages caused by weather can have a big impact on the economy, infrastructure, and quality of life in affected areas. It’s hard to provide early and accurate warnings for these disruptions because severe weather often leads to missing weather recordings, making it difficult to make learning-based predictions. To address this challenge, we have developed HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. This new framework’s multiplex network layers gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. Our study shows that this method effectively improves the accuracy of predicting the duration of weather-related outages. The HMN-RTS model improves 3 h ahead outage severity prediction, resulting in a 0.76 macro F1-score vs 0.51 for the best alternative for a five-class problem formulation. The HMN-RTS model provides useful predictions of outage duration 6 h ahead, enabling grid operators to implement outage mitigation strategies promptly. The results highlight the HMN-RTS’s ability to offer early, reliable, and efficient risk assessment. 

We investigate the statistical learning of nodal attribute distributions in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models, based on preferential attachment is studied, where new nodes form connections dependent on both their attribute values and popularity as measured by degree. We consider several canonical attribute agnostic sampling schemes such as Metropolis-Hasting random walk, versions of node2vec (Grover and Leskovec 2016) that incorporate both classical random walk and non-backtracking propensities and propose new variants which use attribute information in addition to topological information to explore the network. The performance of such algorithms is studied on both synthetic networks and real world systems, and its dependence on the degree of homophily, or absence thereof, is assessed.