| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000431797 |
| 005 | | 20200224105201 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085796354 |
| 035 | |
▼a (MiAaPQ)AAI13887152 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 620 |
| 100 | 1 |
▼a Crnovrsanin, Tarik Esad. |
| 245 | 10 |
▼a Visualizing Large Complex Streaming Networks. |
| 260 | |
▼a [S.l.]:
▼b University of California, Davis.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 126 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Ma, Kwan-Liu. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Davis, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a The growing popularity and diversity of social network applications present new opportunities as well as new challenges. The resulting social networks may have exceptional value in many domains, such as business intelligence, sociological studies, organizational studies, and epidemiological studies. The ability to explore and extract information of interest from the networks is thus crucial. However, these networks can often be large, complex, and/or streaming, which makes it difficult to visualize and understand the network when using conventional methods. This dissertation research focuses on the development of new techniques to address the individual and combined challenges when working on these large, complex, and streaming networks. The resulting tools will allow analysts to mix and match techniques for uncovering hidden structures and trends in the data.In this dissertation, we present several techniques that can be used in tandem to help visualize and analyze large, complex, and/or streaming networks. First, a new sensitivity metric is introduced to rank the importance of a node with respect to the other nodes. This sensitivity metric, combined with the notion of an implicit edge-relationship between two entities that are not directly linked- provides analysts with a new capability to reveal hidden relationships in a network. Second, a visual recommendation design is introduced that combines the same sensitivity metric and collaborative filtering to identify significant nodes when exploring a large graph. Third, a novel, incremental layout design with a refinement scheme is introduced to efficiently lay out online dynamic networks while effectively maintaining the mental map. A large benefit of this refinement technique is that it can be applied independently or together with existing force directed layout methods. Lastly, this work concludes by utilizing the knowledge and techniques from our previous works to create a new visual metaphor for discussion forums, which are large, complex and constantly streamed. Collectively, this dissertation provides the tools to explore increasingly available sets of data that have been previously underutilized. These findings can serve as a jumping-off point to advances towards developing sophisticated visualizations for large, complex and streaming networks. |
| 590 | |
▼a School code: 0029. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Information technology. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0489 |
| 710 | 20 |
▼a University of California, Davis.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0029 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491560
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |