Knowledge graph embedding (KGE) is an efficient and scalable method for knowledge graph completion. However, most existing KGE methods suffer from the challenge of multiple relation semantics, which often degrades their performance. This is because most KGE methods learn fixed continuous vectors for entities (relations) and make deterministic entity predictions to complete the knowledge graph, which hardly captures multiple relation semantics. To tackle this issue, previous works try to learn complex probabilistic embeddings instead of fixed embeddings but suffer from heavy computational complexity. In contrast, this paper proposes a simple yet efficient framework namely the Knowledge Graph Diffusion Model (KGDM) to capture the multiple relation semantics in prediction. Its key idea is to cast the problem of entity prediction into conditional entity generation. Specifically, KGDM estimates the probabilistic distribution of target entities in prediction through Denoising Diffusion Probabilistic Models (DDPM). To bridge the gap between continuous diffusion models and discrete KGs, two learnable embedding functions are defined to map entities and relation to continuous vectors. To consider connectivity patterns of KGs, a Conditional Entity Denoiser model is introduced to generate target entities conditioned on given entities and relations. Extensive experiments demonstrate that KGDM significantly outperforms existing state-of-the-art methods in three benchmark datasets.

This paper proposes a diffusion model to measure the evolution of stakeholders' disaster perceptions by integrating a disaster message model, a stakeholder model, and a stakeholder memory model, which collectively describe the process of information flow. Simulation results show that the rate of forgetting has a significantly negative effect on stakeholders' perceptions and the incremental increase in the number of affected individuals has a positive effect on the maximum level of stakeholders' perceptions, but negative effect on the duration of stakeholders' perceptions. Additionally, a delay effect, a stagnation effect, and a cumulative effect exist in the evolution of stakeholders' perceptions. There is a spike at the beginning of the profile of stakeholders' perceptions in the D amped E xponential M odel. An empirical test supports the validity of this model of stakeholders' disaster perceptions.

Abstract Understanding the growth models of news stories on disasters is a key issue for efficient disaster management. This article proposes a method to identify three growth models: the Damped Exponential Model, the Normal Model, and the Fluctuating Model. This method is proven to be valid using the 112 disasters occurring between 2003 and 2008. The factors that influence the likelihood of the growth models include disaster types, newsworthy material, disaster severity, and economic development of the affected area. This article suggests that disaster decision‐makers can identify the respective likelihood of the three growth models of news stories when a disaster happens, and thereby implement effective measures in response to the disaster situation.