arXiv subjects

Junchang Xin

Publications and source records attributed to Junchang Xin.

3 recordsLinked to original sources

arXiv2026

VTRQ: Enabling Verifiable Trajectory Range Queries in Hybrid-Storage Blockchains

Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited support for trajectory data, lacking authenticated data structures (ADS) that enable efficient verification. For example, ADSs designed for queries on one-dimensional data are unsuitable for queries on multidimensional trajectory data, while ADSs tailored for discrete data may yield incomplete results when applied to continuous trajectory data. We propose the first framework for verifiable trajectory range queries in hybrid-storage blockchains, called VTRQ. It features two efficient ADSs: (i) a spatial ADS for road networks that leverages hierarchical organization to aggregate trajectory, edge, and node hashes, thus reducing redundant computations and improving spatial verification efficiency; and (ii) a temporal ADS based on interval trees, which indexes only the start and end times of trajectories, thereby enabling pruning and efficient temporal verification. By separating spatial and temporal indexing, the method reduces the need for data comparison, enhancing both query and verification efficiency. To aggregate spatial and temporal query results, VTRQ provides a spatio-temporal edge aggregation mechanism that combines temporal verification of spatial nodes, spatial intersection computation, and temporal intersection analysis to achieve spatio-temporal filtering.

cs.DB↗

arXiv2026

GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRank

The algorithms based on message passing neural networks (MPNNs) on graphs have recently achieved great success for various graph applications. However, studies find that these methods always propagate the information to very limited neighborhoods with shallow depth, particularly due to over-smoothing. That means most of the existing MPNNs fail to be so `deep'. Although some previous work tended to handle this challenge via optimization- or structure-level remedies, the overall performance of GCNs still suffers from limited accuracy, poor stability, and unaffordable computational cost. Moreover, neglect of higher-order relationships during the propagation of MPNNs has further limited the performance of them. To overcome these challenges, a novel variant of PageRank named motif-based personalized PageRank (MPPR) is proposed to measure the influence of one node to another on the basis of considering higher-order motif relationships. Secondly, the MPPR is utilized to the message passing process of GCNs, thereby guiding the message passing process at a relatively `high' level. The experimental results show that the proposed method outperforms almost all of the baselines on accuracy, stability, and time consumption. Additionally, the proposed method can be considered as a component that can underpin almost all GCN tasks, with DGCRL being demonstrated in the experiment. The anonymous code repository is available at: https://anonymous.4open.science/r/GCN-MPPR-AFD6/.

cs.AI↗

arXiv2025

RLBayes: a Bayesian Network Structure Learning Algorithm via Reinforcement Learning-Based Search Strategy

The score-based structure learning of Bayesian network (BN) is an effective way to learn BN models, which are regarded as some of the most compelling probabilistic graphical models in the field of representation and reasoning under uncertainty. However, the search space of structure learning grows super-exponentially as the number of variables increases, which makes BN structure learning an NP-hard problem, as well as a combination optimization problem (COP). Despite the successes of many heuristic methods on it, the results of the structure learning of BN are usually unsatisfactory. Inspired by Q-learning, in this paper, a Bayesian network structure learning algorithm via reinforcement learning-based (RL-based) search strategy is proposed, namely RLBayes. The method borrows the idea of RL and tends to record and guide the learning process by a dynamically maintained Q-table. By creating and maintaining the dynamic Q-table, RLBayes achieve storing the unlimited search space within limited space, thereby achieving the structure learning of BN via Q-learning. Not only is it theoretically proved that RLBayes can converge to the global optimal BN structure, but also it is experimentally proved that RLBayes has a better effect than almost all other heuristic search algorithms.

cs.LG↗