Spatial-temporal graph neural networks have demonstrated remarkable performance in time series forecasting. However, their inherent opacity poses a significant challenge, limiting their adoption in high-stakes applications. Existing post-hoc explainers are fundamentally limited, as they operate on static physical topologies and isolated time slices, failing to capture essential semantic relationships and long-range temporal dynamics. To address this, we propose FusionSHAP, a novel model-agnostic explanation framework. FusionSHAP makes two primary contributions. First, it introduces Semantic-Physical Graph Fusion, a process that constructs a hybrid graph by augmenting the conventional physical topology with a semantic graph generated by a Large Language Model. This fusion enables the discovery of non-obvious, functionally-driven relationships between nodes. Second, the framework employs Window-Level Attribution with Causal Lag Alignment, a method that adapts KernelSHAP to a node-timestep feature space. This provides temporally-precise explanations that pinpoint not only which nodes were influential but also when. Experiments on real-world traffic forecasting datasets demonstrate that FusionSHAP generates explanations with demonstrably higher fidelity and sparsity than existing methods, paving a new path toward uncovering the complex, non-local, and temporally-specific dependencies learned by STGNNs.
Clique is a fundamental model for cohesive subgraph discovery and has been extensively studied in single-layer graphs. However, in multilayer (ML) graphs, where vertices are connected through multiple types of relationships, existing studies remain limited and primarily focus on density-based models. In this paper, we propose two novel models, namely skyline ML-clique and optimal ML-clique. Specifically, given an ML graph G, a subgraph S is a skyline ML-clique if it is not dominated by any other clique in terms of both vertex set and supporting layers, while an optimal ML-clique is defined as the subgraph that maximizes a tunable function balancing clique size and layer support. We prove that the problem of enumerating all skyline ML-cliques is NP-hard, and develop a baseline enumeration algorithm along with several optimization techniques, including categorical pruning and adaptive skyline verification. For the optimal ML-clique, we further design a layered branch-and-bound framework with progressive computation to obtain high-quality solutions efficiently. Finally, extensive experiments on 7 real-world ML graphs demonstrate the effectiveness of the proposed algorithms.
Effective spatial keyword queries require joint modeling of location constraints and textual semantics. Existing systems typically rely either on spatial indexes with keyword filters or on semantic embeddings, which treat the two dimensions separately and thus lead to rigid spatial boundaries and limited semantic flexibility. To better understand these limitations, we systematically evaluate six representative approaches on large-scale OpenStreetMap (OSM) data from Greater Melbourne. The results show that non-embedding methods deliver exact matches but fail to generalise semantically, while embedding-based methods broaden coverage but capture spatial context poorly. This trade-off is not inevitable. We contribute two fusion strategies, namely concatenation embedding and contrastive fusion, that map spatial coordinates and textual descriptions into a shared vector space, and we integrate them with PostgreSQL, PostGIS and pgvector for evaluation alongside traditional baselines. Experiments demonstrate that fused embeddings improve semantic recall while preserving spatial fidelity, and they remain stable as query intent shifts between location and meaning. These findings indicate that unified spatial-semantic embeddings provide a practical direction for advancing spatial keyword queries.
Graph neural networks (GNNs) are an emerging tool for analysing graph-structured data, yet their training and inference require access to large and complex graphs. Major organisations such as Google and Facebook hold massive graph datasets (e.g., user interactions/relationships), but cannot share these directly due to their sensitive nature. We propose a secure framework, ColabGNN, for collaborative GNN training and inference that leverages Function Secret Sharing (FSS) to protect both the structure and features of private graphs. Our method ensures that raw graph data, training/inference computations and client queries remain hidden, while still enabling joint model training and secure inference across distributed parties.
This paper proposes a novel method for wheat disease image segmentation under limited labeled data. Rather than relying on traditional fine-tuning strategies, we address the challenge through visual parameter-efficient tuning, enabling pre-trained vision models to adapt effectively to the segmentation of wheat diseases with scarce data. Specifically, we freeze the backbone parameters of the segmentation network to mitigate the need for large-scale training samples. However, wheat disease symptoms exhibit distinct properties compared to natural images, such as randomly distributed lesions and interfering symptoms. To address these domain-specific characteristics, we design a disease-aware adapter that enhances the model’s ability to capture lesion-specific patterns and suppress irrelevant features via introducing class guidance, thereby improving disease segmentation accuracy under limited data conditions. Extensive experiments on wheat disease benchmarks demonstrate that the proposed parameter-efficient wheat disease segmenter achieves superior performance compared with state-of-the-art algorithms.
