Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model’s ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches.

Owing to strong capabilities in capturing interactions among objects and concepts, graph data has been treated as an important type of information collected by smart devices in Industrial Internet of Things (IoT), and the distributed training of graph learning models over these devices brings fundamental supports for intelligent services and operations. However, different IoT devices may collect Non-IID graph data due to different roles in the system, and suffer poor performance when only one unified instance of model is trained. Besides, IoT devices usually belong to different communities in Industrial IoT, such that each community pursues both optimized and rational performance when joining in the training process. Considering both challenges, this article proposes a novel meta-computing framework for federated graph learning in Industrial IoT. A collaborative resource allocation task is formulated where devices belonging to different communities adopt limited resources to participate in the training of multiple instances either within or across communities. Two algorithms are introduced for adaptive and rational resource allocation based on whether devices are owned by single or multiple communities. Both algorithms provide guaranteed performance on efficiency and effectiveness, and the fairness among IoT devices are proved. Finally, extensive numerical results have demonstrated the performance of the proposed framework in handling collaborative graph model learning within Industrial IoT.

Recent research in inductive reasoning has focused on predicting missing links between entities that are not observed during training. However, most approaches usually require that the relations are known at the inference time. In the real world, new entities and new relations usually emerge concurrently, which greatly challenges the model's generalization ability. In this paper, we propose a novel inductive knowledge graph embedding model that effectively handles unknown entities and relations by capturing their local structural features. Specifically, a relation graph is constructed to learn relation representations. In the relation graph, we employ a four-dimensional vector to represent the interaction patterns between nodes (relations), where each dimension corresponds to a specific type of interaction. For entity representations, our model dynamically initializes entity features using relation features and attentively aggregates neighboring features of entities to update entity features. By modeling interaction patterns between relations and incorporating structural information of entities, our model learns how to aggregate neighboring embeddings using attention mechanisms, thus generating high-quality embeddings for new entities and relations. Extensive experiments on benchmark datasets demonstrate that our model outperforms state-of-the-art methods, particularly in scenarios involving completely new relations.

Multi-view graph clustering has been intensively investigated during the past years. However, existing methods are still limited in two main aspects. On the one hand, most of them can not deal with data that have both attributes and graphs. Nowadays, multi-view attributed graph data are ubiquitous and the need for effective clustering methods is growing. On the other hand, many state-of-the-art algorithms are either shallow or deep models. Shallow methods may seriously restrict their capacity for modeling complex data, while deep approaches often involve large number of parameters and are expensive to train in terms of running time and space needed. In this paper, we propose a novel multi-view attributed graph clustering (MAGC) framework, which exploits both node attributes and graphs. Our novelty lies in three aspects. First, instead of deep neural networks, we apply a graph filtering technique to achieve a smooth node representation. Second, the original graph could be noisy or incomplete and is not directly applicable, thus we learn a consensus graph from data by considering the heterogeneous views. Third, high-order relations are explored in a flexible way by designing a new regularizer. Extensive experiments demonstrate the superiority of our method in terms of effectiveness and efficiency.

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.

Crowdsourcing is the perfect show of collective intelligence, and the key of finishing perfectly the crowdsourcing task is to allocate the appropriate task to the appropriate worker. Now the most of crowdsourcing platforms select tasks through tasks search, but it is short of individual recommendation of tasks. Tag-semantic task recommendation model based on deep learning is proposed in the paper. In this paper, the similarity of word vectors is computed, and the semantic tags similar matrix database is established based on the Word2vec deep learning. The task recommending model is established based on semantic tags to achieve the individual recommendation of crowdsourcing tasks. Through computing the similarity of tags, the relevance between task and worker is obtained, which improves the robustness of task recommendation. Through conducting comparison experiments on Tianpeng web dataset, the effectiveness and applicability of the proposed model are verified.