IECA: Intelligent Effective Crawling Algorithm for Web Pages

Obtaining important pages rapidly can be very useful when a crawler cannot visit the entire Web in a reasonable amount of time. Several Crawling algorithms such as Partial PageRank, Batch PageRank, OPIC, and FICA have been proposed, but they have high time complexity or low throughput. To overcome these problems, we propose a new crawling algorithm called IECA which is easy to implement with low time O(E*logV) and memory complexity O(V) -V and E are the number of nodes and edges in the Web graph, respectively. Unlike the mentioned algorithms, IECA traverses the Web graph only once and the importance of the Web pages is determined based on the logarithmic distance and weight of the incoming links. To evaluate IECA, we use three different Web graphs such as the UK-2005, Web graph of university of California, Berkeley-2008, and Iran-2010. Experimental results show that our algorithm outperforms other crawling algorithms in discovering highly important pages.