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The community is not only one kind of widely existing organization in networks, but also contains distinct information about topics. The present paper is to define a metric between community members, weigh their semantic similarity, and finally make use of the metric to find more about the Web. In order to achieve this, the usual practice is to establish a "plane" adjacency matrix according to the citation relationship among all community members. However, it is easy to trigger the problem of topic drift. To overcome this weak point, the present paper puts forward firstly the strategy of establishing a three-order adjacency tensor on the 3-dimensional relationship between the seed document, simple document and the author. Secondly, the adjacency tensor is decomposed to obtain the principal component in each dimension. Thirdly, the semantic similarity between authors is defined. The experiment makes it clear that the semantic similarity between the author and people of importance tends to be stable under a particular circumstance.

For topic distillation and topic exploration, a rich resource is the information from hyper linked Web pages. The anchor text, an objective summary of Web pages, is of strong topic and semantic indication. As to the triangular relationship of the hub Web page, anchor text, and authoritative Web page, a good hub Web page is the one that is directed to a good authoritative Web page through the authoritative anchor text; an authoritative anchor text is the one that connects good hub Web page and good authoritative Web page; and a good authoritative Web page is the one that a good hub Web page directs to through a good authoritative anchor text. This paper is mainly about hypertext-based topic analysis models and community-based topic distillation. Through comparative analysis, it can be concluded that the hypertext-based topic distillation contributes to improving the quality of topic distillation.

Recently, researches on the Hardware and Software Co-Design over IP cores have become a new domain. A critical phase in the HW-SW Co-design process is HW-SW Co-Partitioning which decomposing an expected system into hardware or software parts. This paper proposes an innovative 0-1 partitioning algorithm (named 0-1 algorithm) over IP cores which can efficiently partition an expected system into hardware or software parts. The correctness of the 0- 1algorithm is verified here. It’s illustrated that the result of optimization using the algorithm is better than using genetic algorithm, with similar convergence speed. The algorithm has also the advantage that its convergence is quicker than the ILP (Integer Linear Programming) approach.