The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Solving large FPT problems on coarse-grained parallel machines
Journal of Computer and System Sciences - Special issue on Parameterized computation and complexity
ACM Transactions on Internet Technology (TOIT)
Applying latent dirichlet allocation to group discovery in large graphs
Proceedings of the 2009 ACM symposium on Applied Computing
SEO: Search Engine Optimization Bible
SEO: Search Engine Optimization Bible
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
A constant-factor approximation algorithm for the link building problem
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part II
Maximizing pagerank with new backlinks
CIAC'10 Proceedings of the 7th international conference on Algorithms and Complexity
Parameterized Complexity
On Parameterized Intractability
The Computer Journal
Parameterized Complexity of Cardinality Constrained Optimization Problems
The Computer Journal
| Hi-index | 5.23 |
We consider the LINK BUILDING problem, which involves maximizing the PageRank value of a given target vertex in a directed graph by adding k new links that point to the target (backlinks). We present a theorem describing how the topology of the graph affects the choice of potential new backlinks. Based on this theorem we show that no fully polynomial-time approximation scheme (FPTAS) exists for LINK BUILDING unless P=NP and we also show that LINK BUILDING is W[1]-hard. Furthermore, we show that this problem is in the class APX by presenting the polynomial time algorithm r-Greedy, which selects new backlinks in a greedy fashion and results in a new PageRank value for the target vertex that is within a constant factor from the best possible. We also consider the naive algorithm @p-Naive, where we choose backlinks from vertices with high PageRank values compared to the out-degree and show that this algorithm performs much worse on certain graphs compared to our constant factor approximation. Finally, we provide a lower bound for the approximation ratio of our r-Greedy algorithm.