The nature of statistical learning theory
The nature of statistical learning theory
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Proceedings of the 11th international conference on World Wide Web
Hyperlink Analysis for the Web
IEEE Internet Computing
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Identifying link farm spam pages
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Generalizing PageRank: damping functions for link-based ranking algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A reference collection for web spam
ACM SIGIR Forum
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Active learning with statistical models
Journal of Artificial Intelligence Research
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
COLT'06 Proceedings of the 19th annual conference on Learning Theory
From graphs to manifolds – weak and strong pointwise consistency of graph laplacians
COLT'05 Proceedings of the 18th annual conference on Learning Theory
A boosting algorithm for learning bipartite ranking functions with partially labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Adversarial Information Retrieval on the Web (AIRWeb 2007)
ACM SIGIR Forum
Web spam identification through content and hyperlinks
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Robust PageRank and locally computable spam detection features
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Speeding up algorithms on compressed web graphs
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Web spam identification through language model analysis
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Detecting spam bots in online social networking sites: a machine learning approach
DBSec'10 Proceedings of the 24th annual IFIP WG 11.3 working conference on Data and applications security and privacy
Foundations and Trends in Information Retrieval
The nuts and bolts of a forum spam automator
LEET'11 Proceedings of the 4th USENIX conference on Large-scale exploits and emergent threats
Survey on web spam detection: principles and algorithms
ACM SIGKDD Explorations Newsletter
Automatic seed set expansion for trust propagation based anti-spam algorithms
Information Sciences: an International Journal
Genetic optimized artificial immune system in spam detection: a review and a model
Artificial Intelligence Review
| Hi-index | 0.00 |
Web spam can significantly deteriorate the quality of search engines. Early web spamming techniques mainly manipulate page content. Since linkage information is widely used in web search, link-based spamming has also developed. So far, many techniques have been proposed to detect link spam. Those approaches are basically built on link-based web ranking methods. In contrast, we cast the link spam detection problem into a machine learning problem of classification on directed graphs. We develop discrete analysis on directed graphs, and construct a discrete analogue of classical regularization theory via discrete analysis. A classification algorithm for directed graphs is then derived from the discrete regularization. We have applied the approach to real-world link spam detection problems, and encouraging results have been obtained.