Roles of mixing patterns in the network reconstruction

Reconstructing community structure of online social network via user opinions

User opinion affects the performance of network reconstruction greatly since it plays a crucial role in the network structure. In this paper, we present a novel model for reconstructing the social network with community structure by taking into account the Hegselmann-Krause bounded confidence model of opinion dynamic and compressive sensing method of network reconstruction. Three types of user opinion, including the random opinion, the polarity opinion, and the overlap opinion, are constructed. First, in Zachary's karate club network, the reconstruction accuracies are compared among three types of opinions. Second, the synthetic networks, generated by the Stochastic Block Model, are further examined. The experimental results show that the user opinions play a more important role than the community structure for the network reconstruction. Moreover, the polarity of opinions can increase the accuracy of inter-community and the overlap of opinions can improve the reconstruction accuracy of intra-community. This work helps reveal the mechanism between information propagation and social relation prediction.

A two-stage reconstruction method for complex networked system with hidden nodes

Reconstructing the interacting topology from measurable data is fundamental to understanding, controlling, and predicting the collective dynamics of complex networked systems. Many methods have been proposed to address the basic inverse problem and have achieved satisfactory performance. However, a significant challenge arises when we attempt to decode the underlying structure in the presence of inaccessible nodes due to the partial loss of information. For the purpose of improving the accuracy of network reconstruction with hidden nodes, we developed a robust two-stage network reconstruction method for complex networks with hidden nodes from a small amount of observed time series data. Specifically, the proposed method takes full advantage of the natural sparsity of complex networks and the potential symmetry constraints in dynamic interactions. With robust reconstruction, we can not only locate the position of hidden nodes but also precisely recover the overall network structure on the basis of compensated nodal information. Extensive experiments are conducted to validate the effectiveness of the proposed method and superiority compared with ordinary methods. To some extent, this work sheds light on addressing the inverse problem, of which the system lacks complete exploration in the network science community.

Network reconstruction of social networks based on the public information

The structure of a social network plays a crucial role for dynamic analysis, which is invisible in most scenes. In this paper, we present a model for reconstructing the social network by taking into account the public opinion diffusion dynamic model for specific agenda. First, the initial polarity attitude of users i for the agenda, o_i, is set in the range [-1,1], where negative and positive attitudes are set as -1 and 1, respectively, while 0 means that user i's attitude is uncertain. Second, we present an optimization model for detecting the relationship among each pair of users based on the generated public observable information. The experimental results for four synthetic networks and three real-world social networks show that the reconstruction accuracy depends on the uncertainty of the initial attitudes greatly. This work is helpful for revealing the structure of social networks in terms of public information.