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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 高成炎(Cheng-Yan Kao) | |
dc.contributor.author | Pei-Ying Huang | en |
dc.contributor.author | 黃珮瑩 | zh_TW |
dc.date.accessioned | 2021-06-08T00:59:23Z | - |
dc.date.copyright | 2015-01-28 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-01-23 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18314 | - |
dc.description.abstract | We address the problem of identification of important nodes in the networks. For solving the problem, we propose four social-diversity-dependent schemes to identify important nodes via measuring the influence scores of nodes. They differ in the calculation of social diversities of the mediators. The prior model is based on the community structure. The zero-one spread and weighted spread are based on the static influence propagation while iter-weighted spread considers the dynamic influence spread.
Our findings on synthetic networks suggest that the social diversities of the mediators may play an important role in the identification of important nodes of various influence levels. Comparative analysis shows that iter-weighted spread is superior to our other three methods and PageRank, which implies that dynamic influence propagation may have an effect on discrimination of important nodes. It suggests that the pattern of the influence propagation should be updated dynamically to reflect the flow of influence spread to better capture the rapidly changing dynamics of networks. Inspired by the observations on synthetic networks, we then apply our proposed method to two real-world networks: online social networks (e.g., Twitter) and protein-protein interaction (PPI) networks (e.g., yeast). On Twitter data, we employ iter-weighted spread to identify the influencers. Our results show that iter-weighted spread has a similar performance with PageRank for the high ranked users, while has better results than PageRank for middle ranked users. On yeast data, we proposed a method named Networked Gene Ranker (NGR) integrating gene expression, social diversity and dynamic influence propagation to identify putative candidate genes in yeast PPI networks. Our results on the datasets of AmiGO meiotic genes reveal an interesting observation, node centrality measures perform better than other methods considering the prestige of the mediator. The results on DEG essential genes shows that, in general, NGR performs better than the existing methods. Therefore, we conclude that both of the key mechanisms (i.e., social diversity and dynamic influence propagation) contribute to the detection and discrimination of influencers of difference influence levels in networks (e.g., social networks and PPI networks). Our proposed scheme is therefore practical and feasible to be deployed in the real world. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T00:59:23Z (GMT). No. of bitstreams: 1 ntu-104-D96922004-1.pdf: 6909970 bytes, checksum: 735dadee3367bd45ffca853062ac05dc (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Problem Definition and Motivation 1 1.2 Related Work 4 1.3 Thesis Organization 8 Chapter 2 Identification of Important Nodes in the Networks 9 2.1 Kernel Ideas 9 2.2 Problem Statement 10 2.3 Node Centrality and PageRank 10 2.3.1 Degree Centrality 11 2.3.2 Closeness Centrality 11 2.3.3 Betweenness Centrality 12 2.3.4 PageRank 13 2.4 Social-Diversity-Dependent Influence Algorithms for Identification of Important Nodes in the Network 13 2.4.1 Transition Probability 14 2.4.2 Social Diversity 15 2.4.3 The Prior Model 16 2.4.4 The Influence Propagation Model 18 Chapter 3 A Study on the Effect of Social Diversity on Identification of Important Nodes in Synthetic Networks 23 3.1 Generation of Synthetic Networks 24 3.2 Discrimination of Influencers of Different Influence Level 24 3.3 Effect of Dynamic Influence Propagation 25 Chapter 4 Social Diversity Effect on Identifying Influencers in Social Networks 29 4.1 Kernel Ideas 31 4.2 Problem Statement 31 4.3 Effectiveness of Our Method 32 4.3.1 Datasets 33 4.3.2 Comparative analysis with PageRank and Node Centrality Measures 33 4.3.3 Spearman Correlation Test 37 4.4 Case Study on Twitter 40 4.4.1 Top 20 Ranks 40 4.4.2 Celebrity, Media, and Others 43 Chapter 5 Social Diversity Effect on Identifying Putative Candidate Genes in Biological Networks 47 5.1 Study Overview 48 5.2 Problem Statement 51 5.3 Networked Gene Ranker 52 5.4 Over- and Under- Expressed Genes 52 5.4.1 The Role in Yeast Meiosis 52 5.4.2 The Role in KEGG Yeast Meiotic Pathway 54 5.4.3 The Role in Yeast Protein Complexes 54 5.5 AmiGo Yeast Meiotic Genes 69 5.6 DEG Essential Yeast Genes 70 Chapter 6 Conclusions 71 Bibliography 74 Appendix A. List of Publications 78 | |
dc.language.iso | en | |
dc.title | 以社群多樣性影響力量度探採基因交互網絡之潛在調控成員 | zh_TW |
dc.title | Social-diversity-based Influence Measurements for Mining Putative Regulators in Gene Interaction Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-1 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 賴飛羆(Feipei Lai),翁昭旼(Jau-Min Wong),朱學亭(Hsueh-Ting Chu),李盛安(Sheng-An Lee) | |
dc.subject.keyword | 社群網路,社群多樣性,影響力傳輸,基因交互作用網路,社群多樣性影響力量度, | zh_TW |
dc.subject.keyword | social network,social diversity,influence propagation,protein-protein interaction network,social-diversity-based influence measure, | en |
dc.relation.page | 80 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2015-01-23 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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