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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 鄭士康 | |
| dc.contributor.author | Yi-Kuang Ko | en |
| dc.contributor.author | 柯怡光 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:05:40Z | - |
| dc.date.available | 2010-08-18 | |
| dc.date.copyright | 2010-08-18 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-16 | |
| dc.identifier.citation | [1] Akoglu, L. and C. Faloutsos (2009). RTG: A Recursive Realistic Graph Generator using Random Typing.
[2] Akoglu, L., M. Mcglohon, et al. (2008). RTM: Laws and a recursive generator for weighted time-evolving graphs. [3] Alessandra Sala, L. C., Christo Wilson, Robert Zablit, Haitao Zheng and Ben Zhao (2010). Measurement-calibrated Graph Models for Social Network Experiments. International World Wide Web Conference [4] Anna Dreber, D. G. R., Drew Fudenberg MArtin A. Nowak (2008). Winners don't punish. Nature Letters. [5] Chakrabarti, D., Y. Zhan, et al. (2004). R-MAT: A recursive model for graph mining. In SDM. [6] David M. Pennock, G. W. F., Steve Lawrence Eric J. Glover C. Lee Giles (2001). Winners don't take all: Characterizing the competition for links on the web. PNAS. [7] G. Bianconi 1 A.-L. Barabási 1 (2001). Competition and multiscaling in evolving networks. Europhys Letter. [8] Gueorgi Kossinets1, D. J. W. (2006). Empirical Analysis of an Evolving Social Network. Science. [9] J. Leskovec, C. F. (2007). Scalable Modeling of Real Graphs using Kronecker Multiplication. International Conference on Machine Learning. [10] J. Leskovec, D. C., J. Kleinberg C. Faloutsos (2005). Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication. European Conference on Principles and Practice of Knowledge Discovery in Databases. [11] J. Leskovec, D. C., J. Kleinberg C. Faloutsos Z. Ghahramani. (2010). Kronecker Graphs: An approach to modeling networks. JMLR. [12] J. Leskovec, J. K., C. Faloutsos (2005). Graphs over Time: Laws, Shrinking Diameters and Possible Explanations. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [13] J. Leskovec, J. K., C. Faloutsos (2006). A likelihood approach to analysis of network data. Proceedings of the National Academy of Sciences. [14] J. Leskovec, J. K., C. Faloutsos. (2007). Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data. [15] Jon M. Kleinberg, R. K., Prabhakar Raghavan Sridhar Rajagopalan Andrew S. Tomkins (1999). The Web as a Graph: Measurements, Models, and Methods. SpringerLink,Lecture Notes in Computer Science. [16] Leskovec, J., D. Chakrabarti, et al. (2005). Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication. In PKDD, Springer. [17] Mary McGlohon, L. A., Christos Faloutsos (2006). Graph mining: Laws, generators, and algorithms. ACM Computing Surveys. [18] Mary McGlohon, L. A., Christos Faloutsos (2008). Weighted graphs and disconnected components: patterns and a generator. SIGKDD international conference on Knowledge discovery and data mining. [19] Pandurangan, G., P. Raghavan, et al. (2002). Using PageRank to Characterize Web Structure. INTERNATIONAL CONFERENCE ON COMPUTING AND COMBINATORICS, Springer-Verlag. [20] Paul Erdős, A. R. (1959). On Random Graphs. Publication Mathematica. [21] Pedram Pedarsani, D. R. F., Matthias Grossglauser (2008). Densification Arising from Sampling Fiex Graphs. ACM SIGMETRICS international conference on Measurement and modeling of computer systems. [22] R.Alber, A. L. B. (1999). Emergence of scaling in random networks. Science. [23] Ravi Kumar, J. N., Andrew Tomkins (2006). Structure and Evolution of Online Social Networks. ACM SIGKDD international conference on Knowledge discovery and data mining. [24] Soon-Hyung Yook, H. J., Albert-László Barabási (2002). Modeling the Internet's large-scale topology. PNAS. [25] Tian Bu, D. F. T. (2002). On Distinguishing between Internet Power Law Topology Generators. INFOCOM. [26] U Kang , C. E. T., Christos Faloutsos (2009). 'PEGASUS: A Peta-Scale Graph Mining System - Implementation and Observations.' IEEE International Conference On Data Mining. [27] Watts DJ, S. S. (1998). Collective dynamics of 'small-world' networks. Nature. [28] A., V. (2003). 'Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations.' Phys Rev E Stat Nonlin Soft Matter Phys. [29] Nan Du, C. F., Bai Wang, Leman Akoglu (2009). 'Large human communication networks: patterns and a utility-driven generator.' KDD. [30] Newman, M. E. J. (2003). Random graphs as models of networks. Handbook of Graphs and Networks, S. Bornholdt and H. G. Schuster (eds.), Wiley-VCH, Berlin. [31] Priya Mahadevan, D. K., Kevin Fall, Amin Vahdat (2006). Systematic topology analysis and generation using degree correlations. SIGCOMM 2006. [32] KDD Cup 2003 http://www.cs.cornell.edu/projects/kddcup/datasets.html [33] National Bureau of Economic Research http://www.nber.org/ [34] Takeshi Sakaki, M. O., Yutaka Matsuo (2010). Earthquake Shakes Twitter Users : Real-time Event Detection by Social Sensors WWW. [35] DBLP dataset source ,http://www.informatik.uni-trier.de/~ley/db/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47554 | - |
| dc.description.abstract | 社群網路是一種用來描述個體間彼此關係的表現方式。而研究人員在不同領域的社群網路上面發現了許共通的特性，並且針對這些特性提出各種對應的社群網路產生模型。然而，這些模型多半著重在如何產生類似社群網路的結構特性，而忽略個體間的差異所帶來的影響。本論文藉由分析資料中節點與其鄰居的連結建立時間，說明對於新進入社群網路中的節點，在建立連結時會受到其他節點存在於社群網路的時間長短所影響。並且針對此一分析結果提出一偏好選擇的模型，對於不同概念Random Selecting，Seniority-Similar Selection，Elder-preferred Selection的選擇方式進行模擬。然而實驗的結果顯示，僅靠單一偏好的選擇方式並沒有辦法很好的重現現實資料中所分析出的特性。因此，我們提出一Mix Gaussian Model，藉由同時考慮新穎性與能見度兩種概念方式進行模擬，結果顯示此一方法最能夠符合資料的特性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:05:40Z (GMT). No. of bitstreams: 1 ntu-99-R97921028-1.pdf: 914041 bytes, checksum: 9f7d08e43d8b42f2685cd5662f495ac8 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | List of Figures……………………………………………………………………. …. …4
List of Tables…………………………………………………………………….. …. ...6 Chapter 1. Introduction………………………………………………………………….7 1.1. Background…………………………………………………………………...7 1.2. Motivations…….………………………………………………………..9 1.3. Problem Definition and Proposed Solution…………………………….10 1.4. Contribution…………………………………………………………….11 1.5. Organization……………………………………………………………11 Chapter 2. Related Work……………………………………………………………….12 2.1. Basic Models…………………………………………………………...12 2.2. Preferential Attachment Models………………………………………..14 2.3. Recursive Graph Generation Models…………………………………..17 Chapter 3.Observations: ……………………………………………………………….19 3.1. Dataset Description…………………………………………………….19 3.2. Seniority Difference Distribution………………………………………19 Chapter 4.Proposed Models: …………………………………………………………...24 4.1. Preferential Selecting Model…………………………………………...24 Chapter 5.Experiment Result…………………………………………………………..33 5.1 Seniority Difference Curve…………………………………………….33 5.2 Discussion……………………………………………………………...35 5.2.1 Densification Power Law………………………………………..…36 5.2.2 Shrinking Diameter ………………………………………………..37 Chapter 6.Conclusion…………………………………………………………………..39 6.1 Conclusion……………………………………………………………..39 6.2 Future Work……………………………………………………………39 Bibliography 40 | |
| dc.language.iso | zh-TW | |
| dc.subject | 圖形演化 | zh_TW |
| dc.subject | 社群網路 | zh_TW |
| dc.subject | Social Network | en |
| dc.subject | Graph Evolution | en |
| dc.title | 基於能見度與新穎性的社群網路演化模型 | zh_TW |
| dc.title | A Social Network Evolution Model Based On Visibility and Freshness | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 林守德 | |
| dc.contributor.oralexamcommittee | 張時中,逄愛君 | |
| dc.subject.keyword | 社群網路,圖形演化, | zh_TW |
| dc.subject.keyword | Social Network,Graph Evolution, | en |
| dc.relation.page | 43 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-16 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| Appears in Collections: | 電機工程學系 | |
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| ntu-99-1.pdf Restricted Access | 892.62 kB | Adobe PDF |
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