Self-supervised heterogeneous graph representation learning (SSHGRL) is a key technique for embedding heterogeneous graphs, enabling effective analysis and modeling of social networks and other graph-structured data, which are central to knowledge discovery and the study of social systems. However, existing SSHGRL methods are hardly applied to large-scale heterogeneous graph environments due to the normally used metapath decomposing mechanism being graph-size-sensitive. Moreover, the existing self-supervised signals are normally created from Shared Mutual Information (SMI) of different graph views that ignore the Non-SMI (NMI) contained in the same view. This results in the model tending to learn insufficient graph representation. To this end, this article proposes a designated masking propagation (DMP) mechanism to process heterogeneous graphs without using metapath. Moreover, based on the DMP graph view, a novel sufficient representation is proposed to learn the effective graph representation by combining both NMI and SMI. Extensive experiments on eight large- and medium-scale heterogeneous graph datasets demonstrate the superiority of our method, setting new state-of-the-art performance in various big data contexts.

Arrhythmia diagnosis based on electrocardiogram (ECG) signals is crucial for early screening of cardiovascular diseases and long-term health monitoring, particularly in home and wearable device scenarios. However, due to significant inter-individual differences in ECG signal morphology, the performance of the model drops sharply when applied to unseen individuals. Moreover, short-term monitoring is difficult to capture nocturnal or occasional abnormalities. To overcome these limitations, a cloud-edge-end medical multimedia collaborative auxiliary diagnosis system was studied and constructed. Specifically, we propose an arrhythmia classification algorithm based on unsupervised domain adaptation, which effectively improves the classification accuracy in inter-patient scenarios. In addition, ECG signals are collected using self-developed portable home heart rate monitoring devices, and real-time analysis is conducted by algorithms deployed on mobile devices to generate visual reports. Doctors review them in the cloud, thus establishing a complete closed-loop collaborative auxiliary diagnosis and treatment process. Experimental results show that the proposed algorithm achieves an accuracy of 97.83% on the MITDB dataset, significantly exceeding the existing state-of-the-art methods. After 15 days of testing with 100 volunteers, the system demonstrated a user satisfaction score of 4.7/5.0, stable Bluetooth connectivity, and a battery life of up to 29 hours.

Self-supervised Heterogeneous Graph Representation (SSHGRL) learning is widely used in data mining. The latest SSHGRL methods normally use metapaths to describe the heterogeneous information (multiple relations and node types) to learn the heterogeneous graph representation and achieve impressive results. However, establishing metapaths requires lofty computational costs that are too high for the medium and large graphs. To this end, this paper proposes a Reserving-Masking-Reconstruction (RMR) model that can fully consider heterogeneous information without relying on the metapaths. In detail, we propose a reserving method to reserve to-be-masked nodes' (target nodes) information before graph masking. Second, we split the reserved graph into relation subgraphs according to the type of relations that require much less computational overheads than metapath. Then, the target nodes in each relation subgraph are randomly masked with minimal topology information loss. After, a novel reconstruction method is proposed to reconstruct the masked nodes on different relation subgraphs to establish the self-supervised signal. The proposed method requires low computational complexity and can establish a self-supervised signal without deeply changing the graph topology. Experimental results show the proposed method achieves state-of-the-art records on medium and large-scale heterogeneous graphs and competitive records on small-scale heterogeneous graphs. The code is available at https://github.com/DuanhaoranCC/RMR.

Heterogeneous Information Networks (HINs) are crucial in various intelligent systems. The latest advancements in HIN learning aim to combine meta-paths and hypergraphs, capitalizing on their strengths for further success. However, existing methods typically transform meta-paths into hypergraphs by simply removing the original edges from the meta-paths to integrate two semantics. This will inevitably encounter semantic ambiguity, a so-called semantic-shift problem, during the “meta-path → hyperedges” transforming, causing limited improvements. To address this, we introduce a novel fusion framework that distills knowledge from meta-paths into hypergraphs, mitigating such a problem. Specifically, we propose a unique hyperedge extraction method for constructing the hypergraph, incorporating various aspects instead of relying solely on one type of meta-path. Subsequently, we introduce a shallow student model to capture high-order information from the hypergraph, complementing a teacher model that focuses on encoding low-order information from meta-paths. Then, a distillation framework is employed to integrate explicitly multi-order information into the student. Experimental results across diverse datasets demonstrate a substantial improvement in node classification tasks, with an average accuracy increase of 2.1% over existing state-of-the-art methods.

Abstract Self-supervised graph representation learning has been widely used in many intelligent applications since labeled information can hardly be found in these data environments. Currently, masking and reconstruction-based (MR-based) methods lead the state-of-the-art records in the self-supervised graph representation field. However, existing MR-based methods did not fully consider both the deep-level node and structure information which might decrease the final performance of the graph representation. To this end, this paper proposes a node and edge dual-masked self-supervised graph representation model to consider both node and structure information. First, a dual masking model is proposed to perform node masking and edge masking on the original graph at the same time to generate two masking graphs. Second, a graph encoder is designed to encode the two generated masking graphs. Then, two reconstruction decoders are designed to reconstruct the nodes and edges according to the masking graphs. At last, the reconstructed nodes and edges are compared with the original nodes and edges to calculate the loss values without using the labeled information. The proposed method is validated on a total of 14 datasets for graph node classification tasks and graph classification tasks. The experimental results show that the method is effective in self-supervised graph representation. The code is available at: https://github.com/TangPeng0627/Node-and-Edge-Dual-Mask .