Graphs naturally represent a host of processes, including interactions between people on social or communication networks, links between webpages on the Recommender systems the textbook pdf Wide Web, protein interactions in biological networks, movement in transportation networks, electricity delivery in smart energy grids, relations in bibliographic data, and many others. Understanding the different techniques applicable, including heterogeneous graph mining algorithms, graphical models, latent variable models, matrix factorization methods and more.

Dealing with the heterogeneity of the data. The common need for information integration and alignment. Addressing each of these issues at scale. Traditionally, a number of subareas have contributed to this space: communities in graph mining, learning from structured data, statistical relational learning, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.

Speaker Bio: Leman Akoglu is an assistant professor of Information Systems at the Heinz College of Carnegie Mellon University. She received her PhD from the Computer Science Department at Carnegie Mellon University in 2012. Her research interests involve algorithmic problems in data mining and applied machine learning, focusing on patterns and anomalies, with applications to fraud and event detection. Speaker Bio: Nitesh Chawla is the Frank M. He started his tenure-track career at Notre Dame in 2007, and quickly advanced from assistant professor to a chaired full professor position in nine years. In such heterogeneous information networks, we make a key observation that many interactions happen due to some event and the objects in each event form a complete semantic unit. Speaker Bio: Jiawei Han is Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign.

He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 900 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences. In this talk, I will discuss two paradoxes arising from this discrepancy and show how they can bias analysis of social data. Speaker Bio: Kristina Lerman is Research Team Lead at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Associate Professor in the USC Computer Science Department.

Trained as a physicist, she now applies network analysis and machine learning to problems in computational social science, including crowdsourcing, social network and social media analysis. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data. Speaker Bio: Jiliang Tang is an assistant professor in the computer science and engineering department at Michigan State University since Fall 2016. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015.

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