Influence propagates more widely over social networks, and strongly affects people’s thoughts, opinions, and even behaviors in human life. Influence spread is a more complicated problem that makes the modeling of this process even more challenging. The linear threshold (LT) model provides a simple way to describe the problem. However, this model neglects the decay of individuals’ influence as time lapses. This paper develops a dynamic linear threshold model with decay (DLTD) in which influence decays based on three strategies: (1) Random decay, (2) linear decay, and (3) nonlinear decay. The results on real-world datasets show that compared with the LT model and the independent cascade (IC) model, the DLTD tends to be more accurate especially for the decay with a linear function, and more concentrated to converge to the ground-truth as the size of seed set increases. We also observe that our model is more stable to the networks’ structures. Furthermore, an interesting finding indicates that the balance between community structures’ internal links and external links is more effective for influence spread.

Graph layout can help users explore graph data intuitively. However, when handling large graph data volumes, the high time complexity of the layout algorithm and the overlap of visual elements usually lead to a significant decrease in analysis efficiency and user experience. Increasing computing speed and improving visual quality of large graph layouts are two key approaches to solving these problems. Previous surveys are mainly conducted from the aspects of specific graph type, layout techniques and layout evaluation, while seldom concentrating on layout optimization. The paper reviews the recent works on the optimization of the visual and computational efficiency of graphs, and establishes a taxonomy according to the stage when these methods are implemented: pre-layout, in-layout and post-layout. The pre-layout methods focus on graph data compression techniques, which involve graph filtering and graph aggregation. The in-layout approaches optimize the layout process from computing architecture and algorithms, where deep learning techniques are also included. Visual mapping and interactive layout adjustment are post-layout optimization techniques. Our survey reviews the current research on large graph layout optimization techniques in different stages of the layout design process, and presents possible research challenges and opportunities in the future.