Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common property of these networks is their community structure, considered as clusters of densely connected groups of vertices, with only sparser connections between groups. The identification of such communities relies on some notion of clustering or density measure, which defines the communities that can be found. However, previous community detection methods usually apply the same structural measure on all kinds of networks, despite their distinct dissimilar features. In this paper, we present a new community mining measure, Max-Min Modularity, which considers both connected pairs and criteria defined by domain experts in finding communities, and then specify a hierarchical clustering algorithm to detect communities in networks. When applied to real world networks for which the community structures are already known, our method shows improvement over previous algorithms. In addition, when applied to randomly generated networks for which we only have approximate information about communities, it gives promising results which shows the algorithm’s robustness against noise.

Abstract. This paper introduces a set of challenges for developing a dissemination service over a Web collaborative network. We define specific metrics for working on a co-authorship research social network. As a case study, we build such a network using those metrics and compare it to a manually built one. Specifically, once we build a collaborative network and verify its quality, the overall effectiveness of the dissemination services will also be improved. Key words: Social Networks, Dissemination Systems. 1

Much of the knowledge available on the web today comes as a result of fruitful collaborations among large groups of people. One of the most striking examples of successful web collaboration is the online encyclopedia Wikipedia. The web is used as a collaboration platform by highly specialized blogging communities and by the scientific community. An important reason for the richness of content generated through web collaborations is that the participants in such collaborations are not constrained by geographic location. Thus, like-minded individuals from across the world can join their efforts. This also means, however, that web collaborators often do not know each other, and, thus, finding collaborators on the web is more difficult than it is with more traditional forms of collaboration that are initiated based on acquaintance. This difficulty is further exacerbated by the fact that web collaborations tend to be more dynamic as participants join and abandon communities. We consider the task of recommending project-specific potential collaborators to web users and propose an approach that is based on statistical relational learning. Our proposed model thus has the advantages that it can include complex features composed of multiple properties and relationships of the entities, it can handle the high levels of noise and uncertainty inherent in user actions, and it allows for joint decision-making, which leads to more accurate predictions. To ensure scalability, our model is trained in an online fashion. We demonstrate the effectiveness of our approach on a data set collected from

Abstract. The definitive classification of scientific journals depends on their aim and scope details. In this paper, we present an approach to facilitate the journals classification of the DBLP datasets. For the analysis, the DBLP data sets were pre-processed by assigning each journal attributes defined by its topics. It is subsequently shown how theory of formal concept analysis can be applied to analyze the relations between journals and the extracted topics from their aims and scopes. It is shown how this approach can be used to facilitate the classifications of scientific journals. 1

Abstract—This paper presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science. The developed tool is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. The visualization of co-authorship networks allows to describe the author and his/her current surroundings while still incorporating the historical aspect. The analysis is based on using the forgetting function to hold the information relevant to the selected date. After this analysis, we are capable of computing several measures, which can describe different aspects of user behaviour from the point of view of scientific social network. I.