Topical crawlers are increasingly seen as a way to address the scalability limitations of universal search engines, by distributing the crawling process across users, queries, or even client computers. The context available to such crawlers can guide the navigation of links with the goal of efficiently locating highly relevant target pages. We developed a framework to fairly evaluate topical crawling algorithms under a number of performance metrics. Such a framework is employed here to evaluate different algorithms that have proven highly competitive among those proposed in the literature and in our own previous research. In particular we focus on the tradeoff between exploration and exploitation of the cues available to a crawler, and on adaptive crawlers that use machine learning techniques to guide their search. We find that the best performance is achieved by a novel combination of explorative and exploitative bias, and introduce an evolutionary crawler that surpasses the performance of the best non-adaptive crawler after sufficiently long crawls. We also analyze the computational complexity of the various crawlers and discuss how performance and complexity scale with available resources. Evolutionary crawlers achieve high efficiency and scalability by distributing the work across concurrent agents, resulting in the best performance/cost ratio.

In ongoing research, a collaborative peer network application is being proposed to address the scalability limitations of centralized search engines. Here we introduce a local adaptive routing algorithm used to dynamically change the topology of the peer network based on a simple learning scheme driven by query response interactions among neighbors. We test the algorithm via simulations with 70 model users based on actual Web crawls. We find that the network topology rapidly converges from a random network to a small world network, with emerging clusters that match the user communities with shared interests.