Many researchers have studied complex networks such as the World Wide Web, social networks, and the protein interaction network. They have found scale-free characteristics, the small-world effect, the property of high-clustering coefficient, and so on. One hot topic in this area is community detection. For example, the community shows a set of web pages about a certain topic in the WWW. The community structure is unquestionably a key characteristic of complex networks. In this paper, we propose a new method for finding communities in complex networks. Our proposed method considers the overlaps between communities using the concept of the intersection graph. Additionally, we address the problem of edge inhomogeneity by weighting edges using the degree of overlaps and the similarity of content information between sets. Finally, we conduct clustering based on modularity. And then, we evaluate our method on a real SNS network.

Computing the semantic relatedness between two words or phrases is an important problem in fields such as information retrieval and natural language processing. Explicit Semantic Analysis (ESA), a state-of-the-art approach to solve the problem uses word frequency to estimate relevance. Therefore, the relevance of words with low frequency cannot always be well estimated. To improve the relevance estimate of low-frequency words and concepts, the authors apply regression to word frequency, its location in an article, and its text style to calculate the relevance. The relevance value is subsequently used to compute semantic relatedness. Empirical evaluation shows that, for low-frequency words, the authors’ method achieves better estimate of semantic relatedness over ESA. Furthermore, when all words of the dataset are considered, the combination of the authors’ proposed method and the conventional approach outperforms the conventional approach alone.

Many researchers have studied about complex networks such as the World Wide Web, social networks and the protein interaction network. One hot topic in this area is community detection. For example, in the WWW, the community shows a set of web pages about a certain topic. The community structure is unquestionably a key characteristic of complex networks. We have proposed the novel community extracting method. The method considers the overlaps between communities using the idea of the intersection graph. Additionally, we address the problem of edge inhomogeneity by weiting edges using content information. Finally, we conduct clustering based on modularity. In this paper, we evaluate our method through applying to real microblog networks.

Annotation is getting popular for Web documents. Especially, conditional annotation (annotation to be displayed depending on the user's operations) is useful for users to use educational Web contents. In this paper, we propose W3annotator a system which enables users to make conditional annotations by programming by demonstration. Annotation creators input operation examples in W3Annotator. The system recommends operational events which might be useful for creating annotations. Just by selecting an operational event from the recommended ones and inputting messages, they can create conditional annotations. In the experiment, we asked professional creators to use our system. The results showed that time for inputting operations and messages is shorter than time for considering the content of annotations. They also showed that the users created annotations based on the recommended operational events.