| Project/Area Number |
23500194
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Ryukoku University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
SAITO Kazumi 静岡県立大学, 経営情報学部, 教授 (80379544)
OHARA Kouzou 青山学院大学, 理工学部, 准教授 (30294127)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2011: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 社会ネットワーク分析 / 情報拡散モデル / オピニオン形成モデル / 学習アルゴリズム / 変化点検出 / データマイニング / 複雑ネットワーク科学 |
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| Research Abstract |
We have proposed the value-weighted mixture voter model and the voter model with temporal-decay dynamics as mathematical models for analyzing opinion formation in social networks, and have mathematically clarified their asymptotic behaviors. Also, we have constructed efficient methods of inferring the values of model parameters from observed sequences of opinion diffusion, and have experimentally demonstrated the effectiveness of the methods by using real data. Moreover, we have proposed a variety of applications such as detecting the period in which burst of information and opinion diffusion occurs from an observed diffusion sequence, and predicting missing links in social media data, and have experimentally demonstrated their effectiveness.
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