Now showing items 1-2 of 2

    • gMLC: a multi-label feature selection framework for graph classification 

      Kong, Xiangnan; Yu, Philip S. (Springer Verlag, 2012-05)
      Graph classification has been showing critical importance in a wide variety of applications, e.g. drug activity predictions and toxicology analysis. Current research on graph classification focuses on single-label settings. ...
    • Modeling Big Data Variety with Graph Mining Techniques 

      Kong, Xiangnan (2014-10-28)
      Graphs are ubiquitous and have become increasingly important in modeling diverse kinds of objects. In many real-world applications, instances are not represented as feature vectors, but as graphs with complex structures, ...