Graph Neural Networks (GNNs) are popular for learning node representations in complex graph structures. Traditional methods use Euclidean space but struggle to capture hierarchical structures in real-world graphs. Besides, it’s important to note that in practical applications, many graphs are dynamic and undergo continuous evolution over time. To investigate the characteristics of complex temporal networks, we have introduced a dynamic graph embedding model in the Lorentz space, building upon the foundation of the previously proposed DynHAT model. More specially, our model divides the dynamic graph into multiple discrete static graphs, maps each static graph to the Lorentz space, and then learns informative node representations over time using a self-attention mechanism. We have conducted link prediction experiments on two types of graphs: communication networks and rating networks. Through comprehensive experiments conducted on five real-world datasets, we have demonstrated the superiority of our model in embedding dynamic graphs within Lorentz space.

Graph neural networks (GNNs) have recently become increasingly popular due to their ability to learn node representations in complex graphs. Existing graph representation learning methods mainly target static graphs in Euclidean space, whereas many graphs in practical applications are dynamic and evolve continuously over time. Recent work has demonstrated that real-world graphs exhibit hierarchical properties. Unfortunately, many methods typically do not account for these latent hierarchical structures. In this work, we propose a dynamic network in hyperbolic space via self-attention, referred to as DynHAT, which leverages both the hyperbolic geometry and attention mechanism to learn node representations. More specifically, DynHAT captures hierarchical information by mapping the structural graph onto hyperbolic space, and time-varying dynamic evolution by flexibly weighting historical representations. Through extensive experiments on three real-world datasets, we show the superiority of our model in embedding dynamic graphs in hyperbolic space and competing methods in a link prediction task. In addition, our results show that embedding dynamic graphs in hyperbolic space has competitive performance when necessitating low dimensions.

Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional space, has attracted considerable research attention.Existing heterogeneous graph representation learning methods also take temporal evolution into consideration in Euclidean space which, however, underestimates the inherent complex and hierarchical properties in many real-world temporal networks, leading to sub-optimal embeddings.To explore these properties of a dynamic heterogeneous network, we propose a dynamic hyperbolic heterogeneous embedding(DyHHE) model that fully takes advantage of the hyperbolic geometry and structural heterogeneity.More specially, to capture the structure and semantic relations between nodes, we employ the meta-path guided random walk to sample the sequences for each node.Then DyHHE maps the temporal graph into hyperbolic space, and capture the structural heterogeneity and evolving behaviors by facilitating the proximity measurement.Experimental results on two real-world datasets demonstrate the superiority of DyHHE, as it consistently outperforms competing methods in link prediction task.

Accurate intention understanding of the user inputs is the key to human-computer interaction (HCI). At present, more and more studies just focus on the improvement of algorithm efficiency and ignore the nature exploration of intention understanding. In humans, working memory is regarded as a cognitive system for handling a range of neuro-cognitive tasks. Because the intention understanding is a kind of human cognitive ability, in this paper we will explore the human cognitive execution mechanism and try to apply it to improve the machines' intention understanding level. First, we demonstrated a cognitive learning model called Cortico-Basal ganglia-Cerebella (CBC) loops plays an important role in the process of working memory. Then, based on the full understanding of the loops operation mechanism, we put forward a new model of intension understanding. Finally, we applied this model on speech data and compared it with other two methods. The results showed that the new model could help to get task-specific vectors and offer further gains in performance on intention understanding.