Spam-Resilient Web Rankings via Influence Throttling

Web search is one of the most critical applications for managing the massive amount of distributed Web content. Due to the overwhelming reliance on Web search, there is a rise in efforts to manipulate (or spam) Web search engines. In this paper, we develop a spam-resilient ranking model that promotes a source-based view of the Web. One of the most salient features of our spam-resilient ranking algorithm is the concept of influence throttling. We show how to utilize influence throttling to counter Web spam that aims at manipulating link-based ranking systems, especially PageRank-like systems. Through formal analysis and experimental evaluation, we show the effectiveness and robustness of our spam-resilient ranking model in comparison with existing Web algorithms such as PageRank.