Social networks have become a major focus of research in recent years, initially directed towards static networks but increasingly, towards dynamic ones. In this paper, we investi...
Jerry Scripps, Pang-Ning Tan, Abdol-Hossein Esfaha...
Research in relational data mining has two major directions: finding global models of a relational database and the discovery of local relational patterns within a database. While...
: ? Towards Combining Web Classification and Web Information Extraction: a Case Study Ping Luo, Fen Lin, Yuhong Xiong, Yong Zhao, Zhongzhi Shi HP Laboratories HPL-2009-86 Classific...
Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Spa...
In recent years, the blogosphere has experienced a substantial increase in the number of posts published daily, forcing users to cope with information overload. The task of guidin...
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Motivated by structural properties of the Web graph that support efficient data structures for in memory adjacency queries, we study the extent to which a large network can be com...
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, ...
Given a real, and weighted person-to-person network which changes over time, what can we say about the cliques that it contains? Do the incidents of communication, or weights on t...
Nan Du, Christos Faloutsos, Bai Wang, Leman Akoglu
Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parall...
Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, R...
We present a new approach to large-scale graph mining based on so-called backbone refinement classes. The method efficiently mines tree-shaped subgraph descriptors under minimum f...