Name
Abstract | ||
|---|---|---|
In recent years, many social network services have made lots of business models. However, it has big weakness related with personal information spill or attacks from spammers. There are many conventional approaches about spammer detection. Almost the whole studies suggest solutions by using vertex degrees or Local Clustering Coefficient. However, they have been caused false positive results. In other words, many normal vertices can be mistaken for spammers. Therefore, we propose a new approach by using the circuit of graph structure. And then we demonstrate the strengths of our work by employing the experiment results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIGCOMP.2017.7881677 | 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) |
Keywords | Field | DocType |
graph,spammer,Local Clustering Coefficient,shortest path | Graph,World Wide Web,Social network,Vertex (geometry),Computer science,Business model,Personally identifiable information,Clustering coefficient,Spamming | Conference |
ISSN | ISBN | Citations |
2375-933X | 978-1-5090-3016-3 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chris Soo-Hyun Eom | 1 | 31 | 3.41 |
| Wookey Lee | 2 | 196 | 29.22 |
| Jung Hun Lee | 3 | 2 | 1.79 |
| Wan-Sup CHO | 4 | 80 | 60.04 |