Trip recommendation aims to plan user’s travel based on their specified preferences. Traditional heuristic and statistical approaches often fail to capture the intricate nuances of user intentions, leading to subpar performance. Recent deep-learning methods show attractive accuracy but struggle to generate faithful trajectories that match user intentions. In this work, we propose a DDPM-based incremental knowledge injection module to ensure the faithfulness of the generated trajectories. Experiments on two datasets verify the effectiveness of our approach.

Information diffusion prediction is a critical task for many social network applications. However, current methods are mainly limited by the following aspects: user relationships behind resharing behaviors are complex and entangled. To address these issues, we propose MHGFormer, a novel multi-channel hypergraph transformer framework, to better decouple complex user relations and obtain fine-grained user representations. First, we employ designed triangular motifs to decouple user relations into three different level hypergraphs. Second, a position-aware hypergraph transformer is used to refine user relation and obtain high-quality user representations. Extensive experiments conducted on two social datasets demonstrate that MHGFormer outperforms state-of-the-art diffusion models across several settings.

Document-level Relation Extraction (DocRE) aims to identify relation labels between entities within a single document. It requires handling several sentences and reasoning over them. State-of-the-art DocRE methods use a graph structure to connect entities across the document to capture dependency syntax information. However, this is insufficient to fully exploit the rich syntax information in the document. In this work, we propose to fuse constituency and dependency syntax into DocRE. It uses constituency syntax to aggregate the whole sentence information and select the instructive sentences for the pairs of targets. It exploits dependency syntax in a graph structure with constituency syntax enhancement and chooses the path between entity pairs based on the dependency graph. The experimental results on datasets from various domains demonstrate the effectiveness of the proposed method.

Mining high-quality logical rules is crucial as they can provide beneficial interpretability for predictions. Recent methods that incorporate logical rules into learning tasks have been proven to yield high-quality logical rules successfully. However, existing methods either rely on the rule instances observed to support rule mining, or simply embed the rule head and the rule body to learn from them. Additionally, they can not fully utilize the rich semantic information contained in logical rules and overlook the intrinsic correlations between all relations within the domain. In this paper, we propose a model called Correlation and order-Aware Rule Learning (CARL) that captures deeper semantic information in rules by allowing relations to be co-aware of each other and paying attention to logical sequence sensitivity. CARL utilizes semantic consistency between the rule body and rule head as its learning objective, continuously introducing more semantic information and logically simplifying the rule body while considering logical sequence sensitivity. We explored the internal correlations between domain relations and used the thought of knowledge distillation to simplify modules so that relations in CARL can share or perceive each other’s information or state efficiently. Experiments on link prediction tasks have demonstrated that CARL can learn higher-quality rules and yield state-of-the-art results on four popular public datasets. https://github.com/burning5112/CARL

Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs. Existing models use techniques from image super-resolution and achieve good performance in FUFI. However, they often rely on supervised learning with a large amount of training data, and often lack generalization capability and face overfitting. We present a new solution:Spatial-TemporalContrasting for Fine-Grained UrbanFlow Inference (STCF). It consists of (i) two pre-training networks for spatial-temporal contrasting between flow maps; and (ii) one coupled fine-tuning network for fusing learned features. By attractingspatial-temporally similarflow maps while distancing dissimilar ones within the representation space, STCF enhances efficiency and performance. Comprehensive experiments on two large-scale, real-world urban flow datasets reveal that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than prior arts.

Pedestrian trajectory prediction is one of the critical research issues in road traffic, which helps autonomous vehicles foresee the future paths of pedestrians and accordingly avoid crashes in time. However, the randomness and uncertainty of trajectories is a challenge caused by numerous social rules, various surroundings, and individual intentions of pedestrians. In this paper, we propose a method based on adaptive graph convolutional neural network (AGCN) to process these factors, named social interactions, from spatial and temporal perspectives. Specifically, we employ an LSTM encoder-decoder framework and adopt the AGCN to model the pedestrian spatial interactions per time step from all trajectories. Then, in order to capture the temporal interactions and reduce error accumulation, we introduce an attention mechanism to help focus more on those important moments and integrate the historical trajectory features with a distance-based loss function. We evaluate the performance of our proposed method on various benchmark datasets, and the results show our method achieves better performance compared with several existing methods.

The development of Internet of Things systems (IoTs) and 5G technology has allowed image and text information to be collected and spread at an unprecedentedly high speed. To improve the data processing capabilities of IoTs, the semantic relations between images and text should be extracted efficiently and accurately. Therefore, to reduce the enormous semantic differences between images and text, existing methods introduce consensus knowledge graphs into image–text matching tasks. However, these methods result in noisy edges during the graph construction stage and overlook detailed knowledge extraction, leading to reduced performance in semantic matching. In this article, a two-layer heterogeneous knowledge graph network is proposed to solve the above problems. The proposed model incorporates category knowledge and local knowledge for improved data representation. Specifically, a category-based hierarchical knowledge graph is constructed to learn representations of knowledge concepts through a hierarchical correlation graph embedding (HCGE) module. Then, a globally guided local attention (GLA) module is used to extract fine-grained local knowledge. Finally, the similarity between the input image and text is calculated based on knowledge-fused features to complete the matching process. Extensive experiments show that the proposed model can learn more effective knowledge features to improve the efficacy of image–text matching in.

Internet of Thing (IoT) systems have been treated as a novel platform for graph data acquisition. Contents like dynamic network topology, organization and control flows, and interactions among monitored objects all contribute to the huge volumes of graph data generated in IoT. These data are believed to brought significant benefits to both the operation and functionalities of IoT systems, especially when combined with cutting-edge Artificial Intelligence techniques. However, these graph data are usually locally collected by data contributors with sensing devices, which could be both partially overlapped as they record same environment, and sensitive as they can indicate private physical status of contributors. Considering all challenges, current solutions for graph data collection in IoT are incapable. Therefore, this article proposes a novel framework for privacy-preserving distributed graph data collection for IoT. The framework allows the graphs kept by data contributors to be partially overlapped, and can help the data broker to efficiently derive the universal view by combining these graphs. The differential privacy is applied for privacy preservation during data collection. The proposed problem aims at minimizing the total bandwidth consumption for graph collection, which is proved to be NP-complete. Then three algorithms are proposed for different circumstances, based on the diverse knowledge and purposes held by the data broker. Finally, both theoretical and numerical analysis have demonstrated the advancement of these methods.

This paper proposes a multi-agent system-based grid middleware. There exist three types of agents in the middleware according to different functions. Each type of agent is further constructed to be a multi-agent system. The multi-agent system dispatches a set of individual agents to coordinate a user job over grid in a decentralized manner. In this paper, the middleware's four layers architecture is detailed and multi-agent system work principle is analyzed. Also the individual agent's implementation based on Java is illuminated. Finally the experiment result is presented via comparing the traditional grid with multi-agent system-based grid. The result indicates that our proposed approach can obtain about fifteen percent higher communication performance in the point-to-point communication.