Mining Top-K Influencers in Social Networks on Heterogeneous Communities
碩士 === 國立中正大學 === 資訊工程研究所 === 103 === In this thesis, we discuss how to find top-K influencers in social networks based on the different rates of diffusion between heterogeneous communities. The different rates of diffusion means that when influence spreads to other communities, the influence will b...
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ndltd-TW-103CCU003920252016-08-22T04:18:02Z http://ndltd.ncl.edu.tw/handle/86878388130131857335 Mining Top-K Influencers in Social Networks on Heterogeneous Communities 利用團體異構性質在社群網路中尋找K個最具影響力的人 Hsiang-Pin Wu 吳祥彬 碩士 國立中正大學 資訊工程研究所 103 In this thesis, we discuss how to find top-K influencers in social networks based on the different rates of diffusion between heterogeneous communities. The different rates of diffusion means that when influence spreads to other communities, the influence will be reduced. So our method wants to select persons which have good interpersonal relationships in those communities whose size is larger than the average size. It means intra-community edges and inter-community edges of the selected candidate node both larger than the average of communities. Following the popular persons are selected to be candidates, the candidates have stronger connections in other communities will be selected to be influencers. After selecting influencers, the spread of influence will be simulate by heat diffusion model. In experiments of the different rates of diffusion, two real-world data sets will be used to compare our method with others. The results show that our method has better performance on spread of influence in the large scale of network. Sing-Ling Lee 李新林 2015 學位論文 ; thesis 38 en_US |
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碩士 === 國立中正大學 === 資訊工程研究所 === 103 === In this thesis, we discuss how to find top-K influencers in social networks based on the different rates of diffusion between heterogeneous communities. The different rates of diffusion means that when influence spreads to other communities, the influence will be reduced. So our method wants to select persons which have good interpersonal relationships in those communities whose size is larger than the average size. It means intra-community edges and inter-community edges of the selected candidate node both larger than the average of communities. Following the popular persons are selected to be candidates, the candidates have stronger connections in other communities will be selected to be influencers. After selecting influencers, the spread of influence will be simulate by heat diffusion model. In experiments of the different rates of diffusion, two real-world data sets will be used to compare our method with others. The results show that our method has better performance on spread of influence in the large scale of network.
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Sing-Ling Lee |
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Sing-Ling Lee Hsiang-Pin Wu 吳祥彬 |
author |
Hsiang-Pin Wu 吳祥彬 |
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Hsiang-Pin Wu 吳祥彬 Mining Top-K Influencers in Social Networks on Heterogeneous Communities |
author_sort |
Hsiang-Pin Wu |
title |
Mining Top-K Influencers in Social Networks on Heterogeneous Communities |
title_short |
Mining Top-K Influencers in Social Networks on Heterogeneous Communities |
title_full |
Mining Top-K Influencers in Social Networks on Heterogeneous Communities |
title_fullStr |
Mining Top-K Influencers in Social Networks on Heterogeneous Communities |
title_full_unstemmed |
Mining Top-K Influencers in Social Networks on Heterogeneous Communities |
title_sort |
mining top-k influencers in social networks on heterogeneous communities |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/86878388130131857335 |
work_keys_str_mv |
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