Spammer Detection for Real-Time Big Data Graphs
| Autor: | Chris Soo-Hyun Eom, Jinho Kim, James Jung-Hun Lee, Wookey Lee |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Social network
business.industry Computer science 05 social sciences Big data 050801 communication & media studies Cloud computing Business model computer.software_genre Spamming 0508 media and communications 0502 economics and business Shortest path problem 050211 marketing Data mining business Personally identifiable information computer Clustering coefficient |
| Zdroj: | UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld |
| DOI: | 10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0187 |
| Popis: | Prodigious explosion of social network services may trigger new business models, there trails, however, negative aspects such as personal information spill or spamming as much. Amongst conventional spam detection approaches, the studies based on vertex Degrees have been sacrificed false negative results so that normal vertices can be specified as spammer ones. In this paper, we propose a novel approach by applying the circuit structure in the social networks,, which demonstrates the advantages of our work in the experiment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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