RESUMO
Most current studies on information diffusion in online social networks focus on the deterministic aspects of social networks. However, the behavioral parameters of online social networks are uncertain, unpredictable, and time-varying. Thus, deterministic graphs for modeling information diffusion in online social networks are too restrictive to solve most real network problems, such as influence maximization. Recently, stochastic graphs have been proposed as a graph model for social network applications where the weights associated with links in the stochastic graph are random variables. In this paper, we first propose a diffusion model based on a stochastic graph, in which influence probabilities associated with its links are unknown random variables. Then we develop an approach using the set of learning automata residing in the proposed diffusion model to estimate the influence probabilities by sampling from the links of the stochastic graph. Numerical simulations conducted on real and artificial stochastic networks demonstrate the effectiveness of the proposed stochastic diffusion model for influence maximization.
RESUMO
BACKGROUND: Hepatitis C virus (HCV) infection is a major public health challenge worldwide. Implementing policies to cope with this challenge requires commitment from all stakeholders at various levels, and all necessary resources should be mobilised. Support for various HCV-related stakeholders can reduce the challenges and obstacles that can be encountered during the programme implementation. The present study aims to identify all stakeholders involved with HCV-related policy-making in Iran at different steps (policy development, implementation and evaluation) and to characterise them in terms of interest, position, power and influence, in order to provide valuable information for appropriate decision-making and design. The present study can also serve as a case study for healthcare systems in other countries. METHOD: An approach based on social network analysis was utilised. Data collected included relevant document searches and in-depth interviews to a sample of 18 key informants. RESULTS: Various stakeholders were found to be involved with HCV-related policies in Iran. The extent of their participation and support in policy-making varied. Specifically, international agencies had a high interest for HCV-related policy-making, whereas media and members of the private sector were characterised by a medium interest and governmental and non-governmental bodies by a highly variable interest, ranging from low to high, depending on the specific organism. Moreover, media and members of the private sector, non-governmental institutions and international agencies were rated low in terms of position, whereas governmental actors were rated low to high. Media were rated medium in terms of power, whereas international agencies and members of the private sector were respectively rated low to medium and low. Non-governmental actors were rated low, whilst governmental bodies were rated low to high. Finally, media, members of the private sector and international agencies were rated medium in terms of influence, whereas non-governmental and governmental actors were respectively rated low to medium and low to high. CONCLUSION: Policy-making involves trust, negotiation and integration of the different views of all stakeholders. Social network analysis was critical for identifying stakeholders and showing that, in Iran, involvement in HCV-related policy-making is generally low. This information is of practical implication for policy- and decision-makers regarding the adoption of more favourable and effective strategies.