Using Rank Propagation and Probabilistic Counting for Link-based Spam Detection
author:
Carlos Castillo,
Yahoo! Research
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| Slides | |
| 0:01 | Using Rank Propagation and Probabilistic Counting for Link-based Spam Dete |
| 0:26 | Content |
| 0:54 | What is on the Web? |
| 1:32 | Web spam (keywords + links) |
| 2:20 | Web spam (mostly keywords) |
| 2:44 | Search engine? |
| 3:14 | Fake search engine |
| 3:24 | Problem: “normal” pages that are spam |
| 3:56 | Link farms |
| 5:23 | Motivation |
| 6:01 | Metrics |
| 7:18 | Test collection |
| 8:11 | Degree-based measures |
| 8:16 | Degree |
| 9:01 | Degree |
| 9:14 | Edge reciprocity |
| 10:09 | Assortativity |
| 11:08 | Automatic classifier |
| 12:48 | PageRank |
| 12:50 | PageRank |
| 13:00 | Maximum PageRank in the Host |
| 13:25 | Variance of PageRank |
| 14:15 | Variance of PageRank of in-neighbors |
| 14:39 | Automatic classifier |
| 15:09 | TrustRank |
| 15:18 | TrustRank |
| 16:05 | TrustRank score |
| 16:55 | TrustRank / PageRank |
| 17:13 | Automatic classifier |
| 17:36 | Truncated PageRank |
| 17:42 | Path-based formula for PageRank |
| 18:11 | General functional ranking |
| 18:49 | Truncated PageRank |
| 19:48 | Truncated PageRank(T=2) / PageRank |
| 20:14 | Max. change of Truncated PageRank |
| 20:31 | Automatic classifier |
| 20:51 | Counting supporters |
| 20:55 | Idea: count “supporters” at different distances |
| 21:37 | High and low-ranked pages are different |
| 22:09 | Probabilistic count |
| 22:50 | Hosts at distance 4 |
| 23:07 | Automatic classifier |
| 23:16 | Conclusions |
| 23:21 | Summary of classifiers |
| 25:07 | Top 10 metrics |
| 25:12 | Conclusions |
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