Sucheta Soundarajan

Degree:

  • PhD, Computer Science (2013, Cornell University)

Research Interests:

  • Data mining
  • Social network analysis
  • Community detection
  • Applications to social and life sciences

Current Research:

Dr. Soundarajan’s research focuses on the structure of social and other real-world networks. She is interested in a variety of problems related to social network analysis, including community detection, link prediction, and network similarity. She is currently studying how communities change over time and, in particular, the structural factors that influence a community’s evolution. She is also interested in developing methods to obtain accurate samples of large network.

Courses Taught:

  • CIS 675 (Design and Analysis of Algorithms): Fall 2015

Selected Publications:

Sucheta Soundarajan and John Hopcroft. Use of Local Group Information to Identify Communities in Networks. ACM Transactions on Knowledge Discovery from Data (TKDD). 2015.

Sucheta Soundarajan, Tina Eliassi-Rad, and Brian Gallagher. A Guide to Selecting a Network Similarity Method. SIAM Conference on Data Mining (SDM). 2014.

Bruno Abrahao, Sucheta Soundarajan, John Hopcroft, and Robert Kleinberg. A Separability Framework for Analyzing Community Structure. ACM Transactions on Knowledge Discovery from Data (TKDD-CASIN). 2014.

Bruno Abrahao, Sucheta Soundarajan, John Hopcroft, and Robert Kleinberg. On the Separability of Structural Classes of Communities. 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 2012.

Sucheta Soundarajan and John Hopcroft. Using Community Information to Improve the Precision of Link Prediction Methods. World Wide Web (WWW) 2012.