My research is in learning and mining meaningful network representations of rich datasets, usually in temporal or sequential settings. Examples include building similarity graphs from time series (ICDM’17), reducing large networks to compact descriptions (CSUR’18, SNAM’18), and learning adaptive summaries of relational web data on user devices (ongoing). In general, I try to apply my research to important real-world problems across diverse domains. Recent applications include professional career trajectories (KDD’18), network monitoring, email communications, and more.
For a full list, see my CV.
REGAL: Representation Learning-based Graph Alignment
Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
ACM International Conference on Information and Knowledge Management (CIKM), 2018
Career Transitions and Trajectories: A Case Study in Computing
Tara Safavi, Maryam Davoodi, Danai Koutra
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018
Graph Summarization Methods and Applications: A Survey
Yike Liu, Tara Safavi, Abhilash Dighe, Danai Koutra
ACM Computing Surveys (CSUR), 2018
Scalable Hashing-Based Network Discovery
Tara Safavi, Chandra Sripada, Danai Koutra
IEEE International Conference on Data Mining (ICDM), 2017
Nominated for best paper