Scalable Mining on Information Networks

1. Scalable Mining on Information Networks Investigators: Philip S. Yu, Computer Science Department • Data accumulated at exponential rate across all organizations , all domains, and all geographies • These data often not in structured record format - we focus on graphs and networks • Need to be able to mine the vast amount of data to get useful information and knowledge • Identify distinctive or discriminative substructures in the graph as features • Devise new similarity measures on graphs • Explore graph compression to reduce a huge graph into a smaller one for further analysis • Conduct community mining from multi-relational networks • Capture dynamic and evolutional behavior of networks • Develop real-time processing capability to address monitoring type applications • Graph indexing methods • Similarity search methods for graphs • Data Integration, cleaning and validation techniques in Information Networks • Online Analytical Processing paradigms for Information Networks • Algorithms for mining Information Networks, including social networks • Real-time stream mining algorithms Yeast protein interaction network Co-author network