Tensor Representation-Based Multiview Graph Contrastive Learning for IoE Intelligence

Yuan Gao, Qian Zhao, Laurence T. Yang, Jing Yang, Jieming Yang

Abstract: As a prevalent computing paradigm, graph computing provides an effective service strategy for the analysis of big data within the Internet of Everything (IoE), particularly for clustering graph-structured data in the IoE. Among various methods, graph contrastive clustering, which is devoted to revealing the intrinsic structure of graphs and efficiently grouping nodes into distinct clusters by contrasting positive-negative counterparts, has attracted widely attention in recent years. However, the existing methods seriously ignore the graph topology and node attributes when setting positive and negative sample pairs, which further leads to node semantic inconsistency. To this end, we design a novel tensor representation-based multiview contrastive graph representation learning framework, including adaptive data augmentation, high-confidence sample pairs construction, and a simple yet effective self-optimizing module guided by clustering objective function, to address issues of graph contrastive learning in ignoring complementary information among the topology and attributes. Specifically, by jointly modeling the graph structure and multiview node attributes, the new proposed clustering model can concurrently mine both hard positive and negative samples, and dynamically enhance the weight allocation of the hard samples during the learning process. Then leveraging the characteristics of graph-structured data, we incorporate a small subset of nodes with the highest similarity as additional positive samples to improve the discriminative power of the proposed model. Furthermore, a self-optimizing clustering module is introduced to enhance the algorithm’s performance. Experimental results on four commonly used IoE-related data sets validate that our proposed approach can achieve state-of-the-art clustering performance.

External IDs:dblp:journals/iotj/GaoZYYY25