Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management. While learned indexes improve data structure access, they struggle with interconnected graph data. We present LearnGraph, a novel architecture with an adaptive tree-based memory manager that dynamically optimizes for graph topology and access patterns. Our design integrates two key components: a hierarchical learned index optimized for graph topology to predict vertex and edge locations, and an adaptive tree structure that automatically reorganizes memory regions based on access patterns. Evaluation results demonstrate that LearnGraph outperforms state-of-theart dynamic graph systems, achieving $3.4 \times$ higher throughput on average and reducing processing time by $1.7 \times$ to $11 \times$ across standard graph workloads.

Influence maximization (IM) aims to identify$k$vertices that maximize influence spread across a network. While well-studied in regular graphs, IM in hypergraphs presents unique challenges: conventional graph-based IM methods fail to capture hypergraph-specific structural properties, and existing hypergraph IM algorithms lack theoretical guarantees for time complexity and approximation quality. We address these gaps withHyperIM, a novel algorithm leveraging stratified sampling to generate random reversible reachable sets for efficient seed selection. Our key innovation lies in dual-perspective stratified sampling: assigning sampling probabilities based on vertex structural properties while applying size-adaptive sampling strategies. This approach optimizes seed selection, reduces computational costs, and provides rigorous theoretical guarantees. We further proposeHyperIM_BRR, which optimizes the required number of reversible reachable sets, achieving substantial cost reduction without sacrificing accuracy. Extensive experiments on real-world hypergraphs demonstrate that our algorithms significantly outperform state-of-the-art methods, delivering faster execution times and superior influence spread.

Patient’s understanding on forthcoming dental surgeries is required by patient-centered care and helps reduce anxiety. Due to the complexity of dental surgeries and the patient-dentist expertise gap, conventional techniques of patient education are usually not effective for explaining surgical steps. In this paper, we present OralViewer—the first interactive application that enables dentist’s demonstration of dental surgeries in 3D to promote patients’ understanding. OralViewer takes a single 2D panoramic dental X-ray to reconstruct patient-specific 3D teeth structures, which are then assembled with registered gum and jaw bone models for complete oral cavity modeling. During the demonstration, OralViewer enables dentists to show surgery steps with virtual dental instruments that can animate effects on a 3D model in real-time. A technical evaluation shows that our deep learning model achieves a mean Intersection over Union (IoU) of 0.771 for 3D teeth reconstruction. A patient study with 12 participants shows OralViewer can improve patients’ understanding of surgeries. A preliminary expert study with 3 board-certified dentists further verifies the clinical validity of our system.