• Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC 

      Lin, Zhiyuan; Chau, Duen Horng (Polo) (Georgia Institute of Technology, 2013-08)
      Large graphs with billions of nodes and edges are increasingly common, calling for new kinds of scalable computation frameworks. Although popular, distributed approaches can be expensive to build, or require many resources ...
    • Mage: Expressive Pattern Matching in Richly-Attributed Graphs 

      Pienta, Robert; Tamersoy, Acar; Tong, Hanghang; Chau, Duen Horng (Polo) (Georgia Institute of Technology, 2013)
      Given a large graph with millions of nodes and edges, say a social graph where both the nodes and edges can have multiple different kinds of attributes (e.g., job titles, tie strengths), how do we quickly find matches ...
    • MMAP: Mining Billion-Scale Graphs on a PC with Fast, Minimalist Approach via Memory Mapping 

      Sabrin, Kaeser Md.; Lin, Zhiyuan; Chau, Duen Horng (Polo); Lee, Ho; Kang, U. (Georgia Institute of Technology, 2013)
      Large graphs with billions of nodes and edges are increasingly common, calling for new kinds of scalable computation frameworks. State-of-the-art approaches such as GraphChi and TurboGraph recently demonstrated that a ...
    • VisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Data 

      Choo, Jaegul; Lee, Changhyun; Clarkson, Edward; Liu, Zhicheng; Lee, Hanseung; Chau, Duen Horng (Polo); Li, Fuxin; Kannan, Ramakrishnan; Stolper, Charles D.; Inouye, David; Mehta, Nishant; Ouyang, Hua; Som, Subhojit; Gray, Alexander; Stasko, John; Park, Haesun (Georgia Institute of Technology, 2013)
      We present a visual analytics system called VisIRR, which is an interactive visual information retrieval and recommendation system for document discovery. VisIRR effectively combines both paradigms of passive pull through ...