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.

The fundamental purpose of network embedding is to automatically encode each node in a network as a low-dimensional vector, while at the same time preserving certain characteristics of the network. Based on the nodes' embeddings, downstream network analytic tasks such as community mining, node classification, and link prediction, can then be easily implemented using traditional machine learning methods. In recent years, extensive network embedding methods have been proposed based on factorization, random walks, deep learning, and so on. However, most of them focus mainly on preserving the structural proximity of network nodes, where highly interconnected nodes in a network will be represented closely together in the embedded vector space. While in many real-world networks, existing studies have revealed that high-order organizations (e.g., network motifs and graphlets) may be related to specific network functions. In this case, nodes far apart but with a similar organization in a network (i.e., structural equivalence) may have similar network functions. Accordingly, in this paper, we present a hybrid embedding method that unifies both structural proximity and equivalence (SPaE) of a network. Specifically, we adopt the concept of graphlet degree vector (GDV) to measure structural equivalence between network nodes. Through carrying out experiments on both synthetic and real-world datasets, we evaluate the performance of the hybrid embedding method in tasks of node clustering, node classification, and visualization. The results demonstrate that the proposed SPaE method outperforms several state-of-the-art methods when the network analytic tasks are not merely related to structural proximity. Finally, we also conduct experiments to evaluate the flexibility, robustness, and parameter sensitivity of the hybrid embedding method.

Voluntary vaccination reflects how individuals weigh the risk of infection and the cost of vaccination against the spread of vaccine-preventable diseases, such as smallpox and measles. In a homogeneously mixing population, the infection risk of an individual depends largely on the proportion of vaccinated individuals due to the effects of herd immunity. While in a structured population, the infection risk can also be affected by the structure of individuals’ social network. In this paper, we focus on studying individuals’ self-organizing behaviors under the circumstance of voluntary vaccination in different types of social networks. Specifically, we assume that each individual together with his/her neighbors forms a local well-mixed environment, where individuals meet equally often as long as they have a common neighbor. We carry out simulations on four types of locally-mixed social networks to investigate the network effects on voluntary vaccination. Furthermore, we also evaluate individuals’ vaccinating decisions through interacting with their “neighbors of neighbors”. The results and findings of this paper provide a new perspective for vaccination policy-making by taking into consideration human responses in complex social networks.

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.

Identifying the sources of propagation in social networks, such as the misinformation propagation, is one of the key issues recently. Most existing studies assume the underlying propagation model is known, which is difficult to obtain in practice. Recent efforts have been devoted to detect multiple sources in real-world situations, and the social influence of neighbors in the propagation is assumed to be identical. However, this assumption will result in inaccurate results as the infection state of a node is determined by its critical neighbors. In this paper, we fill this gap by capturing social influence of neighbors with structural properties in social networks. For instance, opinions are more likely to spread via closely connected friends within small groups. Here we propose a Motif-based Graph Convolutional Networks for Source Identification (MGCNSI) framework based on the GCN-based source identification approach. Specifically, different network motifs are used to capture different types of structural properties. Then each motif extracts the critical neighbors of a particular type, and a motif-based graph convolutional layer is constructed to aggregate critical neighbors for that motif. To adapt to underlying propagation mechanisms, an attention mechanism for aggregation is designed to automatically assign higher weights to more informative motifs. The empirical results demonstrate that MGCNSI outperforms several benchmark methods on both synthetic and real-world networks. The advantage is most obvious for networks with denser node neighborhoods, where MGCNSI can select critical neighbors from the larger neighbor sets. How the motifs can capture the social influence and the underlying critical paths of propagation is also illustrated.