Recently, with the development of temporal knowledge graph technology, more and more Temporal Knowledge Graph Embedded (TKGE) models have been developed. The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns and capture specific information about entities and relations. However, existing approaches can capture only some of them with insufficient modeling capacity, and none has a “deep” architecture for modeling the entries in a quadruple at the same dimension. In this article, we propose a more powerful KGE framework named DuCape , which combines a dual quaternion and capsule network in modeling for the first time to make up for the defects of existing TKGE models. In dual quaternion vector space, the head entity learns a k -dimensional rigid transformation parametrized by relation and time, falling near its corresponding tail entity. Further, we employ the embeddings of entities, relations, and time trained from dual quaternion vector space as the input to capsule networks. Experimental results on several basic datasets show that the DuCape model constructed in this article is superior to existing state-of-the-art models.

As the scope of receptive field and the depth of Graph Neural Networks (GNNs) are two completely orthogonal aspects for graph learning, existing GNNs often have shallow layers with truncated-receptive field and far from achieving satisfactory performance. In this article, we follow the idea of decoupling graph convolution into propagation and transformation processes, which generates representations over a sequence of increasingly larger neighborhoods. Though this manner can enlarge the receptive field, it has two critical problems unsolved: how to find the suitable receptive field to avoid under-smoothing or over-smoothing? and how to balance different diffusion operators for better capturing the local and global dependencies? We tackle these challenges and propose a S calable, A daptive G raph C onvolutional N etworks ( SAGCN ) with Transformer architecture. Concretely, we propose a novel non-heuristic metric method that quickly finds the suitable number of diffusing iterations and produces smoothed local embeddings that enable the truncated receptive field to become scalable and independent of prior experience. Furthermore, we devise smooth2seq and diffusion-based position schemes introduced into Transformer architecture for better capturing local and global information among embeddings. Experimental results show that SAGCN enjoys high accuracy, scalability and efficiency on various open benchmarks and is competitive with other state-of-the-art competitors.

Temporal Knowledge Graph Embedding (TKGE) is a crucial technique for performing Temporal Knowledge Graph Completion (TKGC). The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns. However, as we know, most existing TKGE models usually embed KGs into a single geometric space such as Euclidean, hyperbolic or hyperspherical space to maintain their specific geometric structures (e.g., chain, hierarchy, and ring structures). None of the existing methods can simultaneously model relation patterns of chain, hierarchy, ring structures, and relation mapping properties. This paper constructs a hybrid interaction TKGE model HyIE, which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. HyIE performs two Householder transformations of head and tail entities parameterized by relations in a high-dimensional mixed vector space. The curvature of hyperbolic and hyperspherical spaces depends on the product of both relation and temporal. The core of HyIE lies in implementing transformations and interactions of vectors in Euclidean, hyperbolic and hyperspherical spaces, and Household transformation of head and tail entities. Theoretically, HyIE can model crucial relation patterns and mapping properties simultaneously. Experimental results on five temporal knowledge graph benchmarks show that our HyIE achieves state-of-the-art performance.

Detecting anomalies on attributed graphs is a challenging task since labelled anomalies are highly labour-intensive by taking specialized domain knowledge to make anomalous samples not as available as normal ones. Moreover, graphs contain complex structure information as well as attribute information, leading to anomalies that can be typically hidden in the structure space, attribute space, and the mix of both. In this paper, we propose a novel model for graph anomaly detection named ProGAD. Specifically, ProGAD takes advance of label propagation to infer high-quality pseudo labels by considering the structure and attribute inconsistencies between normal and abnormal samples. Meanwhile, ProGAD introduces the prior knowledge of class distribution to correct and refine pseudo labels with a prototype-aware strategy. Experiments demonstrate that ProGAD achieves strong performance compared with the current state-of-the-art methods.