A new mini-batch strategy using aggregated relational data is proposed to fit the mixed membership stochastic blockmodel (MMSB) to large networks. The method uses nodal information and stochastic gradients of bipartite graphs for scalable inference. The approach was applied to a citation network with over two million nodes and 25 million edges, capturing explainable structure. Why it matters: This research enables more efficient community detection in massive networks, which is crucial for analyzing complex relationships in various domains, but this article has no clear connection to the Middle East.
community detection · MMSB · Bayesian network · stochastic variational inference · aggregated relational data
Get the weekly digest
Top AI stories from the GCC region, every week.
arXiv ·
The paper introduces Duet, a hybrid neural relation understanding method for cardinality estimation. Duet addresses limitations of existing learned methods, such as high costs and scalability issues, by incorporating predicate information into an autoregressive model. Experiments demonstrate Duet's efficiency, accuracy, and scalability, even outperforming GPU-based methods on CPU.
arXiv ·
This paper introduces a unified deep autoregressive model (UAE) for cardinality estimation that learns joint data distributions from both data and query workloads. It uses differentiable progressive sampling with the Gumbel-Softmax trick to incorporate supervised query information into the deep autoregressive model. Experiments show UAE achieves better accuracy and efficiency compared to state-of-the-art methods.
arXiv ·
This paper introduces an interpretable pipeline that integrates mobility and social media data to analyze human behavior during crises. The framework was evaluated through two case studies, including a longitudinal analysis of UAE COVID-19 behavior from March 2020 to December 2021. The pipeline aligns heterogeneous daily signals, transforms them into binary behavioral states, applies Formal Concept Analysis (FCA) to extract co-occurrence structures, and mines association rules. Results demonstrate clear cross-domain behavioral structures in crises, yielding both scientifically credible and policy-actionable intelligence. Why it matters: This work provides a novel methodological approach for developing actionable crisis management strategies by fusing multimodal data, directly applicable to public health and emergency response in the UAE and the broader region.