Diffusion on social networks refers to the process where opinions are spread via the connected nodes. Given a set of observed information cascades, one can infer the underlying diffusion process for social network analysis. The independent cascade model (IC model) is a widely adopted diffusion model where a node is assumed to be activated independently by any one of its neighbors. In reality, how a node will be activated also depends on how its neighbors are connected and activated. For instance, the opinions from the neighbors of the same social group are often similar and thus redundant. In this paper, we extend the IC model by considering that: 1) the information coming from the connected neighbors are similar and 2) the underlying redundancy can be modeled using a dynamic structural diversity measure of the neighbors. Our proposed model assumes each node to be activated independently by different communities (or components) of its parent nodes, each weighted by its effective size. An expectation maximization algorithm is derived to infer the model parameters. We compare the performance of the proposed model with the basic IC model and its variants using both synthetic data sets and a real-world data set containing news stories and Web blogs. Our empirical results show that incorporating the community structure of neighbors and the structural diversity measure into the diffusion model significantly improves the accuracy of the model, at the expense of only a reasonable increase in run-time.

Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses on identifying the next user to be influenced. While prior research often concentrates on one of these aspects, a few tackle both concurrently. These two tasks provide complementary insights into the diffusion process at different levels, revealing common traits and unique attributes. The exploration of leveraging common features across these tasks to enhance information prediction remains an underexplored avenue. In this paper, we propose an intuitive and effective model that addresses both macroscopic and microscopic prediction tasks. Our approach considers the interactions and dynamics among cascades at the macro level and incorporates the social homophily of users in social networks at the micro level. Additionally, we introduce adversarial training and orthogonality constraints to ensure the integrity of shared features. Experimental results on four datasets demonstrate that our model significantly outperforms state-of-the-art methods.

Information diffusion prediction is an important task which attempts to predict the potential users that will be affected in an information cascade. Existing studies utilize the user interactions in the diffusion graphs with past diffusion records and user relationships in social networks, and capture the structural proximity of those networks to model user similarities for diffusion prediction. However, they ignore structural similarity of the networks, namely user roles, which is of great significance. Actually, a user's future reposting behavior depends on the roles of previous participants. For instance, sometimes people may not respond to discussions of their so-so friends until a more influential friend joins in. Also, user roles change over time. In this paper, we propose a novel Role-based Dynamic Graph Convolutional Network (RD-GCN) which captures the dependencies of dynamic user roles and information diffusion, and jointly learns the two parts. Specifically, the original diffusion graph is divided into subgraphs according to timestamps and users' preferences and roles are captured from those sequential subgraphs and social networks. Also, gated mechanisms are introduced to incorporate users' different tendencies to be influenced by roles. The experiments on three public datasets demonstrate that RD-GCN significantly outperforms state-of-the-art models, verifying the effectiveness of our proposed model.

Previous studies have pointed out that it is hard to achieve the level of herd immunity for the population and then effectively stop disease propagation from the perspective of public health, if individuals just make vaccination decisions based on individualism. Individuals in reality often exist in the form of groups and cooperate in or among communities. Meanwhile, society studies have suggested that we cannot ignore the existence and influence of collectivism for studying individuals’ decision-making. Regarding this, we formulate two vaccination strategies: individualistic strategy and collectivist strategy. The former helps individuals taking vaccination action after evaluating their perceived risk and cost of themselves, while the latter focuses on evaluating their contribution to their communities. More significantly, we propose a reinforcement learning mechanism based on policy gradient. Each individual can adaptively pick one of these two strategies after weighing their probabilities with a two-layer neural network whose parameters are dynamically updated with his/her more and more vaccination experience. Experimental results on scale-free networks verify that the reinforcement learning mechanism can effectively improve the vaccine coverage level of communities. Moreover, communities can always get higher total payoffs with fewer costs paid, comparing that of pure individualistic strategy. Such performance mostly stems from individuals’ adaptively picking collectivist strategy. Our study suggests that public health authorities should encourage individuals to make vaccination decisions from the perspective of their local mixed groups. Especially, it is more worthy of noting that individuals with low degrees are more significant as their vaccination behaviors can more sharply improve vaccination coverage of their groups and greatly reduce epidemic size.