Energy efficiency has become a critical requirement, driving recommendation systems for resource-constrained environments such as edge devices. Model quantization offers an effective way to build low-bitwidth models while preserving accuracy. However, user–item interaction graphs contain numerous nodes and complex topological structures, leading nodes to exhibit unique similarities and differences. Existing quantization methods uniformly process parameters in high-dimensional DNN layers (e.g., linear, convolutional, or attention layers), while inadequately capturing such similarities among node embeddings. This paper proposes GraphQ, a chunk-wise quantization framework for graph collaborative filtering that supports both the training and post-training phases in a unified perspective. Our core idea is to adaptively partition node embeddings into multiple chunks based on the distribution of embedding values, and then apply chunk-wise quantization. Specifically, for quantization-aware training (QAT), we introduce learnable low-precision quantization factors that partition node embeddings into multiple chunks and are dynamically updated following message passing. For post-training quantization (PTQ), we first cluster nodes and then partition their dimensions into chunks for weight clipping. Extensive experiments on four real-world datasets show that GraphQ outperforms state-of-the-art QAT methods by an average of 27.49% in Recall@10 under the 256-dimensional embedding and 2-bit settings, and surpasses PTQ methods by 78.64% on average under 4-bit settings.

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Given a source, destination, and required keywords, the Optimal Route with Collective Spatial Keywords ( ORCSK ) query aims to find the shortest route covering all keywords. Existing Point of Interest (POI) candidate set-based and path expansion-based methods frequently produce inferior route quality or excessive time overhead, particularly under large-scale query keywords. To address this challenge, we introduce the DO framework, which pioneers the employ Deep Reinforcement Learning for the ORCSK. Specifically, DO first integrates the spatial index with the H2H index to generate and refine high-quality candidate sets. Subsequently, DO utilizes a Transformer-based model to determine the optimal route from the sets. To effectively combine spatial distance and POI attributes, we propose a novel dual-cross encoder architecture. Furthermore, leveraging this architecture, we introduce a multi-route generating strategy, exploiting parallel computing to enhance route quality. Our experiments on real-life road networks demonstrate superior route quality and response time compared to the state-of-the-art method, with an average improvement of 1-2 orders of magnitude in response time, and maintain high efficiency even under large-scale query keywords or dynamic POI attributes scenarios.

Accurate spatial-temporal (ST) traffic prediction plays an essential role in intelligent transportation systems. Existing advanced traffic prediction methods typically utilize spatial-temporal graph neural networks (STGNNs) to capture the ST correlations and achieve excellent prediction performance. However, our experimental investigation reveals that existing static and dynamic graph-based STGNNs still incur excessive noise and redundancy, and fail to discover robust and reliable ST correlations in traffic networks. Moreover, most methods cannot explain the underlying reasons behind the ST correlations. To solve these problems, we propose a novel S patial- T emporal G raph M odeling framework via A daptive contrastive learning (ST-GMA). Firstly, we design a robust augmentation learning module to generate high-level and robust data augmentations via a self-supervised task for modeling reliable correlations. Then, we develop an adaptive contrastive learning module to update correlation graphs by effectively selecting positive and negative augmentations, reducing redundant calculations, and providing insights into the correlation changes. Finally, ST-GMA integrates the generated correlation graphs with ST convolution blocks to conduct traffic prediction tasks. Experimental results on five real-world datasets demonstrate that ST-GMA not only achieves significant prediction performance compared with state-of-the-art methods but also exhibits a new perspective on the interpretability of correlation changes.

Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream graph mining tasks. However, achieving structure fairness in dynamic graph embedding remains an open problem. Neglecting degree changes in dynamic graphs will significantly impair embedding effectiveness without notably improving structure fairness. This is because the embedding performance of high-degree and low-to-high-degree vertices will significantly drop close to the generally poorer embedding performance of most slightly changed vertices in the long-tail part of the power-law distribution. We first identify biased structural evolutions in a dynamic graph based on the evolving trend of vertex degree and then propose FairDGE, the first structurally Fair Dynamic Graph Embedding algorithm. FairDGE learns biased structural evolutions by jointly embedding the connection changes among vertices and the long-short-term evolutionary trend of vertex degrees. Furthermore, a novel dual debiasing approach is devised to encode fair embeddings contrastively, customizing debiasing strategies for different biased structural evolutions. This innovative debiasing strategy breaks the effectiveness bottleneck of embeddings without notable fairness loss. Extensive experiments demonstrate that FairDGE achieves simultaneous improvement in the effectiveness and fairness of embeddings.

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method.

POI recommendation is practically important to facilitate various Location-Based Social Network (LBSN) services, and has attracted rising research attention recently. Existing works generally assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. However, in real application scenarios, the check-in data can be rather unreliable (e.g. sparse, incomplete and inaccurate) due to both subjective and objective causes including positioning error and user privacy concerns. The data uncertainty issue may lead to significant negative impacts on the performance of the POI recommendation, but is not fully explored by existing works. To this end, this paper investigates a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network (BayMAN for short) to effectively address it. Specifically, we construct three POI graphs to comprehensively model the dependencies among the POIs from different views, including the personal POI transition graph, the semantic-based POI graph and distance-based POI graph. As the personal POI transition graph is usually sparse and sensitive to noise, we design a Bayes-enhanced spatial dependency learning module for data augmentation from the local view. A Bayesian posterior guided graph augmentation approach is adopted to generate a new graph with collaborative signals to increase the data diversity. Then both the original and the augmented graphs are used for POI representation learning to counteract the data uncertainty issue. Next, the POI representations of the three view graphs are input into the proposed multi-view attention-based user preference learning module. By incorporating the semantic and distance correlations of POIs, the user preference can be effectively refined and finally robust recommendation results are achieved. We conduct extensive experiments over three real-world LSBN datasets. The results show that BayMAN significantly outperforms the state-of-the-art methods in POI recommendation when the available check-ins are incomplete and noisy.

Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected human/vehicle trajectory flows are usually rather unreliable, may contain various noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it. Specifically, we propose an improved reverse diffusion strategy. A spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are also proposed. Then, we overlay the spatial-temporal feature map extracted by STFormer onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information encompassing the entirety of urban data in fine-grained inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.

Dynamic graph embedding learns representation vectors for vertices and edges in a graph that evolves over time. We aim to capture and embed the evolution of vertices' temporal connectivity. Existing work studies the vertices' dynamic connection changes but neglects the time it takes for edges to evolve, failing to embed temporal linkage information into the evolution of the graph. To capture vertices' temporal linkage evolution, we model dynamic graphs as a sequence of snapshot graphs, appending the respective timespans of edges (ToE). We co-train a linear regressor to embed ToE while inferring a common latent space for all snapshot graphs by a matrix-factorization-based model to embed vertices' dynamic connection changes. Vertices' temporal linkage evolution is captured as their moving trajectories within the common latent representation space. Our embedding algorithm converges quickly with our proposed training methods, which is very time efficient and scalable. Extensive evaluations on several datasets show that our model can achieve significant performance improvements, i.e. 22.98% on average across all datasets, over the state-of-the-art baselines in the tasks of vertex classification, static and time-aware link prediction, and ToE prediction.

Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of such dynamics where the evolution of each local structure starts at different times and lasts for various durations. To maintain asynchronous structural evolutions within the graph, we innovatively formulate dynamic graphs as temporal edge sequences associated with joining time of vertices (ToV) and timespan of edges (ToE). Then, a time-aware Transformer is proposed to embed vertices’ dynamic connections and ToEs into the learned vertex representations. Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information. Extensive evaluations on several datasets show that our approach outperforms the state-of-the-art in a wide range of graph mining tasks. At the same time, it is very efficient and scalable for embedding large-scale dynamic graphs.

Balanced rule-constrained resource allocation aims to evenly distribute tasks to different processors under allocation rule constraints. Conventional heuristic approach fails to achieve optimal solution while simple brute force method has the defect of high computational complexity. To address these limitations, we propose recursive balanced k-subset sum partition (RBkSP), in which iterative 'cut-one-out' policy is employed that in each round, only one subset whose weight of tasks sums up to 1/k of the total weight of all tasks is taken out from the set. In a single partition, we first create a dynamic programming table with its elements recursively computed, then use 'zig-zag search' method to explore the table, find out elements with optimal subset partition and assign different partitions to proper places. Next, to resolve conflicts during allocation, we use simple but effective heuristic method to adjust the allocation of tasks that is contradicted to allocation rules. Testing results show RBkSP can achieve more balanced results with lower computational complexity over classical benchmarks.