With the expansion of the number of users in online social networks, the diversity of users and community characteristics become more prominent. Hypernetwork theory provides a path for characterizing complex relationships in networks. This paper used hypergraph’s hyperedges to represent the community relationship between users, and created an online social hypernetwork information dissemination model (UHIR model) based on user and information attributes by combining the hypernetwork model with the SEIR model. Through this model, this article simulated and analyzed the dynamic process and laws of information dissemination under different network structures, and studied the influence of user influence, confidence, interest value, and information timeliness of the process. The simulation results show that this model can accurately describe the information dissemination trend and process in the real online social network. This work extends a new research direction of information dissemination in hypernetworks and contributes to the in-depth study of more complex information dissemination mechanisms.

With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome (PPI) to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes. Across seven different cancer types, the proposed algorithm always achieves high prediction accuracy, which is superior to the existing advanced methods. In the analysis of the predicted results, about 40 percent of the top 10 candidate genes overlap with the Cancer Gene Census database. Interestingly, the feature comparison indicates that the network based features are still more important than the biological features, including the mutation frequency and genetic differential expression. Further analyses also show that the integration of network structure attributes and biological information is valuable for predicting new cancer driver genes.

The lack of studying the complex organization of directed network usually limits the understanding of the underlying relationship between network structures and functions. Structural controllability and structural predictability, two seemingly unrelated subjects, are revealed in this article to be both highly dependent on the critical links previously thought to only be able to influence the number of driver nodes in controllable directed networks. Here, we show that critical links can not only contribute to structural controllability but can also have a significant impact on the structural predictability of networks, suggesting the universal pattern of structural reciprocity in directed networks. In addition, it is shown that the fraction and location of critical links have a strong influence on the performance of prediction algorithms. Moreover, these empirical results are interpreted by introducing the link centrality based on corresponding line graphs. This work bridges the gap between the two independent research fields, and it provides indications of developing advanced control strategies and prediction algorithms from a microscopic perspective.

MOTIVATION: Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging. RESULTS: We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Link prediction is a fundamental problem in social systems, including the prediction of user interaction and the links between users and items, which is also referred to as recommendation. Previous works mainly focus on the prediction task independently, which predict either the links between users or the links between users and items. However, these two prediction tasks are always coupled with each other, where users' preferences will influence the formation of users' interaction in coupled social networks, and vice versa. In this paper, we proposed a multitasking factor graph model (MFG) for predicting the user interaction and links between users and items simultaneously. Firstly, we observed some interesting network transfer structures according to the feature analysis between the user-user network and user-item network. Sequently, we developed a MFG model based on these network transfer structure, which can transfer information mutually from one network to the other to solve the multitasking prediction problem. Extensive experiments conducted on two real-world coupled social networks demonstrate the effectiveness of our methodology, compared with some other baseline algorithms.

A disease propagating in a community or a rumor spreading in a social network can be described by a contact network whose nodes are persons or centers of contagion and links heterogeneous relations among them. Suppose that a disease or a rumor originating from a single source among a set of suspects spreads in a network, how to locate this disease/rumor source? This problem is crucial and challenging in different fields of computer or social networks, which is made more difficult in many applications where we have access only to a limited set of observations. We study the problem of estimating the origin of a disease/rumor outbreak: given a contact network and a snapshot of epidemic spread at a certain time, root out the infection source. Assuming that the epidemic spread follows the usual susceptible-infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present an algorithm which utilizes the correlated information between the network structure (shortest paths) and the diffusion dynamics (time sequence of infection). The numerical results of artificial and empirical networks show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the disease/rumor spreading process not only in the local particular regime but also for the whole general network.