| LDR | | 01757nmm uu200385 4500 |
| 001 | | 000000331697 |
| 005 | | 20240805164919 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438414563 |
| 035 | |
▼a (MiAaPQ)AAI10932892 |
| 035 | |
▼a (MiAaPQ)ucla:17195 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 510 |
| 100 | 1 |
▼a Boyd, Zachary Mark. |
| 245 | 10 |
▼a Community Detection Using Total Variation and Surface Tension. |
| 260 | |
▼a [S.l.] :
▼b University of California, Los Angeles.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 104 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B. |
| 500 | |
▼a Adviser: Andrea Bertozzi. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
| 520 | |
▼a In recent years, a massive expansion in the amount of available network data in fields such as social networks, food networks in ecology, similarity networks in machine learning, transportation networks, brain networks, and many others has motiv |
| 520 | |
▼a Two of the most well-known frameworks for community detection are modularity optimization and stochastic block modeling. They can often uncover meaningful community structure in networks from diverse applications. However, both of these approach |
| 590 | |
▼a School code: 0031. |
| 650 | 4 |
▼a Mathematics. |
| 690 | |
▼a 0405 |
| 710 | 20 |
▼a University of California, Los Angeles.
▼b Mathematics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-02B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0031 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15001119
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |