SIAM Journal on Computing
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
Local methods for estimating pagerank values
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
To randomize or not to randomize: space optimal summaries for hyperlink analysis
Proceedings of the 15th international conference on World Wide Web
Link spam detection based on mass estimation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Local Graph Partitioning using PageRank Vectors
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Robust PageRank and locally computable spam detection features
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
False-name-proofness in social networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Foundations and Trends in Information Retrieval
The laplacian paradigm: emerging algorithms for massive graphs
TAMC'10 Proceedings of the 7th annual conference on Theory and Applications of Models of Computation
Learning influence in complex social networks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Motivated by the problem of detecting link-spam, we consider the following graph-theoretic primitive: Given a webgraph G, a vertex v in G, and a parameter δ ∈ (0, 1), compute the set of all vertices that contribute to v at least a δ fraction of v's PageRank. We call this set the δ-contributing set of v. To this end, we define the contribution vector of v to be the vector whose entries measure the contributions of every vertex to the PageRank of v. A local algorithm is one that produces a solution by adaptively examining only a small portion of the input graph near a specified vertex. We give an efficient local algorithm that computes an Ɛ-approximation of the contribution vector for a given vertex by adaptively examining O(1/Ɛ) vertices. Using this algorithm, we give a local approximation algorithm for the primitive defined above. Specifically, we give an algorithm that returns a set containing the δ-contributing set of v and at most O(1/δ) vertices from the δ/2-contributing set of v, and which does so by examining at most O(1/δ) vertices. We also give a local algorithm for solving the following problem: If there exist k vertices that contribute a ρ-fraction to the PageRank of v, find a set of k vertices that contribute at least a (ρ-Ɛ)-fraction to the PageRank of v. In this case, we prove that our algorithm examines at most O(k/Ɛ) vertices.