Inferring, Summarizing and Mining Large-scale Graph Data
Networks naturally capture a host of real-world interactions, from social interactions and email communication to brain activity. However, graphs are not always directly observed, especially in scientific domains, such as neuroscience, where monitored brain activity is often captured as time series. How can we efficiently infer networks from time series data (e.g., model the functional organization of brain activity as a network) and speed up the network construction process to scale up to millions of nodes and thousands of graphs? Further, what can be learnt about the structure of graph? How can we summarize its most important properties by taking into account the properties of other graphs in that domain (e.g., neuroscience)? In this talk I will present our recent work on scalable algorithms for inferring, summarizing and mining large collections of graph data. I will also discuss applications in various domains, including connectomics and social science.
Danai Koutra is an Assistant Professor in Computer Science and Engineering at University of Michigan, Ann Arbor. Her research interests include large-scale graph mining, graph similarity and matching, graph summarization, and anomaly detection. Danai's research has been applied mainly to social, collaboration and web networks, as well as brain connectivity graphs. She holds one "rate-1" patent and has six (pending) patents on bipartite graph alignment. Danai won the 2016 ACM SIGKDD Dissertation Award and an honorable mention for the SCS Doctoral Dissertation Award (CMU). She has multiple papers in top data mining conferences, including 2 award-winning papers, and her work has been covered by the popular press, such as the MIT Technology Review. She earned her Ph.D. and M.S. in Computer Science from CMU in 2015 and her diploma in Electrical and Computer Engineering at the National Technical University of Athens in 2010.