In this paper, we report on a large-scale study of structural differences among the national webs. The study is based on a web-scale crawl conducted in the summer 2008. More specifically, we study two graphs derived from this crawl, the nation graph, with nodes corresponding to nations and edges - to links among nations, and the host graph, with nodes corresponding to hosts and edges - to hyperlinks among pages on the hosts. Contrary to some of the previous work [2], our results show that webs of different nations are often very different from each other, both in terms of their internal structure, and in terms of their connectivity with other nations.

This paper considers the problem of identifying on the Web compound documents (cDocs) -- groups of web pages that in aggregate constitute semantically coherent information entities. Examples of cDocs are a news article consisting of several html pages, or a set of pages describing specifications, price, and reviews of a digital camera. Being able to identify cDocs would be useful in many applications including web and intranet search, user navigation, automated collection generation, and information extraction.In the past, several heuristic approaches have been proposed to identify cDocs [1][5]. However, heuristics fail to capture the variety of types, styles and goals of information on the web, and do not account for the fact that the definition of a cDoc often depends on the context. This paper presents an experimental evaluation of three machine learning-based algorithms for cDoc discovery. These algorithms are responsive to the varying structure of cDocs and adaptive to their application-specific nature. Based on our previous work [4], this paper proposes a different scenario for discovering cDocs, and compares in this new setting the local machine learned clustering algorithm from [4] to a global purely graph based approach [3] and a Conditional Markov Network approach previously applied to noun coreference task [6]. The results show that the approach of [4] outperforms the other algorithms, suggesting that global relational characteristics of web sites are too noisy for cDoc identification purposes.