Hypergraph Neural Networks (HGNNs) build on Graph Neural Networks (GNNs) by using hyperedges to capture complex, high-order relationships in data. However, training HGNNs on large hypergraphs is limited by computational and memory bottlenecks on a single machine. To overcome this, we propose LCHGNN, a distributed training method based on a new data structure called the communication graph, which simplifies hypergraph communication by representing cut hyperedges as vertices for structured message passing. LCHGNN employs a vertex-centric, hyperedge-replication-based storage scheme and introduces specialized forward and backward propagation mechanisms tailored for distributed execution. To mitigate communication overhead, We propose a lightweight optimization strategy that employs full synchronization in the initial round, followed by lightweight synchronization in subsequent rounds. Additionally, we present a learnable semi-supervised synchronization (LSS) aggregation mechanism for adaptive hyperedge selection. Extensive experiments on benchmark datasets demonstrate that LCHGNN preserves training accuracy while substantially reducing communication costs and enhancing scalability. This work addresses a critical gap in distributed HGNN research by delivering a communication-efficient and scalable training method, thereby facilitating the application of hypergraph learning to large-scale problems.

Abstract Hyperspace hashing which is often applied to NoSQL data-bases builds indexes by mapping objects with multiple attributes to a multidimensional space. It can accelerate processing queries of some secondary attributes in addition to just primary keys. In recent years, the rich computing resources of GPU provide opportunities for implementing high-performance HyperSpace Hash. In this study, we construct a fully concurrent dynamic hyperspace hash table for GPU. By using atomic operations instead of locking, we make our approach highly parallel and lock-free. We propose a special concurrency control strategy that ensures wait-free read operations. Our data structure is designed considering GPU specific hardware characteristics. We also propose a warp-level pre-combinations data sharing strategy to obtain high parallel acceleration. Experiments on an Nvidia RTX2080Ti GPU suggest that GHSH performs about 20-100X faster than its counterpart on CPU. Specifically, GHSH performs updates with up to 396 M updates/s and processes search queries with up to 995 M queries/s. Compared to other GPU hashes that cannot conduct queries on non-key attributes, GHSH demonstrates comparable building and retrieval performance.

Keyword search is a wildly popular way for querying XML document. However, the increasing volume of XML data poses new challenges to keyword search processing. Parallel database is an efficient solution for this problem. In this paper, we study the problem of effective keyword search for SLCA (Smallest lower common ancestor) in parallel XML databases. We propose two efficient algorithm SONB (Scan once with no buffer) and MSOP (Merge strategy based on ordered partition) to compute the SLCA efficiently in the parallel environment. We have performed an extensive experimental study and the results show that our proposed approach achieves high efficiency for the keyword search.

In recent years, there has been a growing need for location-based services (LBS), ranging from resource tracking to personal life assistance. The Moving k Nearest Neighbor (MkNN) query is a main problem in the LBS area, which retrieves the top k nearest neighbors while the query consumer moves. Considerable attention is attracted to this query type and a large number of studies are proposed recently. Most of these studies focus on ideal Euclidean plane where any two points are visible. In this paper, we propose a method to process MkNN queries in areas with obstacles, which is more realistic. We propose the obstacle-free safe region (OSR) concept within which a query point has the same kNN. No additional processing is required when the query consumer moves inside one OSR. Several novel properties are also introduced and employed in our efficient MkNN processing algorithm. Our experimental results show that our approach handles the MkNN queries with high accuracy and works efficiently.