線上社群網路提供使用者分享並傳遞彼此的資訊 (包括廣告、影片、遊戲、文字、病毒), 資訊將隨著時間動態散播於社群網路, 為了想了解資訊傳遞之後的結果, 本論文提出分析模型來分析資訊在線上社群網路隨著時間散播的程度, 受到病理學理論的啟發, 本模型考慮人的行為以及社群網路的結構, 透過分析與模擬, 我們發現我們所提出的模型可成功的分析資訊在線上社群網路隨時間動態傳播的強度；病毒式行銷是散播資訊的一種方法, 廣告商可透過此行銷手法將資訊散播於線上社群網路, 病毒式行銷的核心概念是選擇適當的使用者做為推薦者來幫助廣告商散播資訊於社群網路, 然而社群網路是個動態改變的網路, 推薦者與使用者之間的社交關係會因為傳遞資訊的結果而改變, 因此社交動態將影響推薦者傳播資訊行為, 本論文將引用賽局理論探討並分析推薦者傳播的行為, 並進一步提出推薦者選擇演算法來探討如何選擇適當的推薦者來幫助廣告商大量的散播資訊於線上社群網路, 透過模擬的結果, 我們的演算法可以達到相當好的效率與效果。

Online social networks (OSNs) are among the most popular sites and communication tools which allow human interact with each other and disseminate information over the Internet. A generic and reliable model is required to capture the information dissemination dynamics of interactions in social networks. Inspired from epidemiology, we present an analytical model to capture the information dissemination dynamics in OSNs. Validated by simulations, the proposed model serves successfully approximating the knowledge of information dissemination dynamics in OSNs.
A viral-marketing-based approach was proposed to identify the most influential users (referrals) to disseminate information in OSNs. In our works, we present a game-theoretic framework to model user behavior of disseminating information due to the effect of social dynamics. Consider the effect of social dynamics and social interaction using OSNs, a referral selection algorithm is presented to maximize the popularity of information. Validated the simulations, the proposed algorithm can achieve the better performances in different application scenarios.

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