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.

Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream learning tasks. For homogeneous graphs, the latest works use original node features to synthesize a similarity graph that can correct the structure of the noised graph. This idea is based on the homogeneity assumption, which states that similar nodes in the homogeneous graph tend to have direct links in the original graph. However, similar nodes in heterogeneous graphs usually do not have direct links, which can not be used to correct the original noise graph. This causes a significant challenge in noised heterogeneous graph learning. To this end, this paper proposes a novel synthesized similarity-based graph neural network compatible with noised heterogeneous graph learning. First, we calculate the original feature similarities of all nodes to synthesize a similarity-based high-order graph. Second, we propose a similarity-aware encoder to embed original and synthesized graphs with shared parameters. Then, instead of graph-to-graph supervising, we synchronously supervise the original and synthesized graph embeddings to predict the same labels. Meanwhile, a target-based graph extracted from the synthesized graph contrasts the structure of the metapath-based graph extracted from the original graph to learn the mutual information. Extensive experiments in numerous real-world datasets show the proposed method achieves state-of-the-art records in the noised heterogeneous graph learning tasks. In highlights, +5~6\% improvements are observed in several noised datasets compared with previous SOTA methods.

Self-supervised graph representation Learning (SSGRL) is an emerging technique for machine learning-based expert applications. SSGRL can effectively encode unlabeled data into machine-understandable knowledge embeddings that can be used in downstream expert tasks. Recently, Masked-Graph-Model (MGM) has achieved extraordinary performances in the field of SSGRL. However, MGM inevitably suffers from a so-called Negative-Migration (NM) problem on unbalanced datasets for specific tasks such as graph classification. The NM problem forces the MGM to predict (reconstruct) the dominant nodes causing the ignoring of the minority but critical nodes. To this end, a novel online prediction module is proposed to dynamically predict the nodes with extended contexts that enable minority nodes can have more weights in the model. Based on the online prediction module, a graph representation learning model is proposed to alleviate the NM problem. Extensive experiments demonstrate that the model outperforms state-of-the-art methods on unbalanced datasets and achieves competitive performance on balanced datasets. Moreover, extra experiments demonstrate that the proposed method requires less computational resource overhead than existing methods. The code is available at: https://github.com/DuanhaoranCC/SimGOP.

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.

Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these classes in model training. The ZSL has been widely used in many knowledge-based models and applications to predict various parameters, including categories, subjects, and anomalies, in different domains. Nonetheless, most existing ZSL methods require the pre-defined semantics or attributes of particular data environments. Therefore, these methods are difficult to be applied to general data environments, such as ImageNet and other real-world datasets and applications. Recent research has tried to use open knowledge to enhance the ZSL methods to adapt it to an open data environment. However, the performance of these methods is relatively low, namely the accuracy is normally below 10%, which is due to the inadequate semantics that can be used from open knowledge. Moreover, the latest methods suffer from a significant "semantic gap" problem between the generated features of unseen classes and the real features of seen classes. To this end, this paper proposes a multi-view graph representation with a similarity diffusion model, applying the ZSL tasks to general data environments. This model applies a multi-view graph to enhance the semantics fully and proposes an innovative diffusion method to augment the graph representation. In addition, a feature diffusion method is proposed to augment the multi-view graph representation and bridge the semantic gap to realize zero-shot predicting. The results of numerous experiments in general data environments and on benchmark datasets show that the proposed method can achieve new state-of-the-art results in the field of general zero-shot learning. Furthermore, seven ablation studies analyze the effects of the settings and different modules of the proposed method on its performance in detail and prove the effectiveness of each module.

Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. A key technology to seamlessly integrate different IoT devices into an IoT system is IoT middleware, a software system layer designed to be the intermediary between IoT devices and applications. However, two issues,communication gapandheterogeneous access, prevent the existing IoT middleware from effective application in an IoT system with heterogeneous standards and interfaces. To address this problem, inspired by a graph-based knowledge system for eliminating heterogeneity in business systems, we, in this article, propose a knowledge graph-based multilayer IoT middleware. The proposed multilayer IoT middleware introduces a new layer to bridge the gap between IoT devices with different communication protocols. It is able to uniformly manage all IoT devices by using an IoT knowledge graph. We evaluate the applicability of the proposed approach by a real-life IoT project, a remote monitoring project of rural sewage treatment stations located in Yunnan Province, China. We find that the proposed approach effectively resolves the communication gap and heterogeneous access problems that occurred in the system.