Graph pattern queries (GPQ) over RDF graphs extend basic graph patterns to support variable-length paths (VLP), thereby enabling complex knowledge retrieval and navigation. Generally, variable-length paths describe the reachability between two vertices via a given property within a specified range. With the increasing scale of RDF graphs, it is necessary to design a partitioning method to achieve efficient distributed queries. Although many partitioning strategies have been proposed for large RDF graphs, most existing methods result in numerous inter-partition joins when processing GPQs, which impacts query performance. In this paper, we formulate a new partitioning problem, MaxLocJoin, aims to minimize inter-partition joins during distributed GPQ processing. For MaxLocJoin, we propose a partitioning framework (PIP) based on property-induced subgraphs, which consist of edges with a specific set of properties. The framework first finds a locally joinable property set using a cost-driven algorithm, LJPS, where the cost depends on the sizes of weakly connected components within its property-induced subgraphs. Subsequently, the graph is partitioned according to the weakly connected components. The framework can achieve two key objectives: first, it enables complete local processing of all variable-length path queries (eliminating inter-partition joins); second, it can minimize the number of inter-partition joins required for traditional graph pattern queries. Moreover, we identify two types of independently executable queries (IEQ): the locally joinable IEQ and the single-property IEQ. After that, a query decomposition algorithm is designed to transform all GPQ into one of them for independent execution in distributed environments. In experiments, we implement two prototype systems based on Jena and Virtuoso, and evaluate them over both real and synthetic RDF graphs. The results show that MaxLocJoin achieves performance improvements from 2.8x to 10.7x over existing methods.

Spatial crowdsourcing (SC) is becoming increasingly popular recently. As a critical issue in SC, task assignment currently faces challenges due to the imbalanced spatiotemporal distribution of tasks. Hence, many related studies and applications focusing on cross-platform task allocation in SC have emerged. Existing work primarily focuses on the maximization of total revenue for inner platform in cross task assignment. In this work, we formulate a SC problem called Cross Dynamic Task Assignment (CDTA) to maximize the overall utility and propose improved solutions aiming at creating a win-win situation for inner platform, task requesters, and outer workers. We first design a hybrid batch processing framework and a novel cross-platform incentive mechanism. Then, with the purpose of allocating tasks to both inner and outer workers, we present a KM-based algorithm that gets the accurate assignment result in each batch and a density-aware greedy algorithm with high efficiency. To maximize the revenue of inner platform and outer workers simultaneously, we model the competition among outer workers as a potential game that is shown to have at least one pure Nash equilibrium and develop a game-theoretic method. Additionally, a simulated annealing-based improved algorithm is proposed to avoid falling into local optima. Last but not least, since random thresholds lead to unstable results when picking tasks that are preferentially assigned to inner workers, we devise an adaptive threshold selection algorithm based on multi-armed bandit to further improve the overall utility. Extensive experiments demonstrate the effectiveness and efficiency of our proposed algorithms on both real and synthetic datasets.

Graph-structured data appears in domains such as molecular analysis, social networks, and program optimization, where graphs often exhibit implicit heterogeneity, as nodes may look homogeneous in type yet differ significantly in semantics or functionality. Graph Neural Networks (GNNs), while powerful on homophilic graphs, tend to degrade in such settings due to polarity confusion, over-smoothing, and inefficiency caused by dense propagation. We propose a polarity-aware framework for graph classification that addresses these challenges through adaptive directional sparse aggregation. The framework introduces a polarity-aware propagation mechanism that adaptively reinforces or inverts neighbor signals, mitigating contamination under heterophily. A polarity-guided sparse aggregation operator further alleviates over-smoothing, improves scalability by constraining redundant connections, and condenses information flow into more effective representations, while maintaining unbiased estimation with controlled variance. We provide theoretical analyses that characterize the computational complexity, stability properties, and expressive behavior of signed directional aggregation, offering theoretical insights into its computational, stability, and expressive properties. Extensive experiments on molecular and social graph benchmarks with implicit heterophily demonstrate consistent improvements in graph classification accuracy and efficiency. Our method achieves a 2.36% improvement when compared with the strongest baseline on each dataset. In addition, it improves accuracy by 4.53% on average on program optimization strategy recognition tasks, reaching 80.12% overall.