| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2020-11-18 |
| 書誌情報 |
en : PEER-TO-PEER NETWORKING AND APPLICATIONS
巻 12,
号 5,
p. 1385-1402,
発行日 2019
|
| タイトル |
|
|
タイトル |
QuickSquad: A new single-machine graph computing framework for detecting fake accounts in large-scale social networks |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Security of online social networks |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Fake accounts |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Sybil detection |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Graph computing |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Distributed system |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
JIANG, Xinyang
LI, Qiang
MA, Zhen
董, 冕雄
WU, Jun
GUO, Dong
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Graph-based approaches for fake account detection is one of the important means to fight against fake accounts' attacks on social networks. With the growth of the scale of social networks, more and more researchers begin to use the graph computing framework to boost their detection algorithms. We make detailed analyses of social networks' graph data and state-of-the-art graph computing frameworks, and find that some techniques of the current graph computing systems are overgeneralized and suboptimal, which means they only focus on how to design a graph processing framework on general graphs but miss the optimization of social networks graphs. So, in this paper we propose QuickSquad, a graph computing system on a single server which is specific to the optimization of social networks graph structures. QuickSquad uses the method of "divide and rule" instead of overgeneralization. First, we divide the graph structure data into the heavy set and the light set according to the out-degree of vertices. Then, we 1) store them with different formats, 2) process them with edge-based updating and vertex-based updating appropriately in a two-phase processing model, 3) apply two selective scheduler strategies of different level, i.e. vertex-level and file-level, and 4) provide four cache priorities when the memory is not enough to cache all data. Finally, we implement two detection methods, dSybilRank and dCOLOR, on our system, and the experiments demonstrate that our system can increase the performance up to 5.91X (from 1.14X) compared with the performance of the current graph computing systems, like GridGraph. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
SPRINGER |
|
言語 |
en |
| 出版者版へのリンク |
|
|
表示名 |
10.1007/s12083-018-0697-2 |
|
URL |
https://doi.org/10.1007/s12083-018-0697-2 |
| DOI |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
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|
関連識別子 |
10.1007/s12083-018-0697-2 |
| 日本十進分類法 |
|
|
主題Scheme |
NDC |
|
主題 |
007 |
| ISSN |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
1936-6442 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
This is a post-peer-review, pre-copyedit version of an article published in QuickSquad: A new single-machine graph computing framework for detecting fake accounts in large-scale social networks. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12083-018-0697-2 |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
|
内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
PPNA_2019_12_1385_1402 (2.5 MB)
