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Special talk by Arijit Khan on Data Management for Emerging Problems in Large Networks

2021.02.11 | Søs Küster Markussen

Date Mon 22 Feb
Time 10:00 11:00
Location Online - Microsoft teams

Special talk by Arijit Khan on Data Management for Emerging Problems in Large Networks


Graphs are widely used in many application domains, including social networks, knowledge graphs, biological networks, software collaboration, geo-spatial road networks, interactive gaming, among many others. One major challenge for graph querying and mining is that non-professional users are not familiar with the complex schema and information descriptions. It becomes hard for users to formulate a query (e.g., SPARQL or exact subgraph pattern) that can be properly processed by the existing systems. As an example, Freebase that powers Google’s knowledge graph alone has over 22 million entities and 350 million relationships in about 5428 domains. Before users can query anything meaningful over this data, they are often overwhelmed by the daunting task of attempting to even digest and understand it. Without knowing the exact structure of the data and the semantics of the entity labels and their relationships, can we still query them and obtain the relevant results? In this talk, I shall give an overview of our user-friendly and scalable techniques for querying big graphs, including heterogeneous networks, uncertain and stream graphs. In the second half of the talk, I shall discuss our newest progress with uncertain graphs: How viral marketing can be effectively employed to maximize the revenue of a social network host, and how to identify the top-r product features that maximize the spread of influence from the seed users to a set of target customers. I shall conclude with my current and future research directions by decoupling graph storage from query processors and by developing smart query routing algorithms for scalable, distributed graph processing systems, human-in-the-loop network exploration, and applications of uncertain graphs in crowd-sourcing.


Arijit Khan is an assistant professor (tenure-track) in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He earned his PhD from the Department of Computer Science, University of California, Santa Barbara, USA, and did a post-doc in the Systems group at ETH Zurich, Switzerland. Arijit is the recipient of the prestigious IBM PhD Fellowship in 2012-13. He published more than 50 papers in premier databases and data mining conferences and journals including ACM SIGMOD, PVLDB, IEEE TKDE, IEEE ICDE, SIAM SDM, WWW, USENIX ATC, EDBT, and ACM CIKM. Arijit co – presented tutorials on emerging graph queries and big graph systems at IEEE ICDE 2012, and at VLDB (2017, 2015, and 2014). He served in the program committee of ACM KDD, ACM SIGMOD, PVLDB, IEEE ICDE, IEEE ICDM, AAAI, SIAM SDM, EDBT, ACM CIKM, and in the senior program committee of WWW. Arijit served as the co-chair of Big-O(Q) workshop co-located with VLDB 2015, wrote a book on uncertain graphs in Morgan & Claypool’s Synthesis Lectures on Data Management. He contributed invited chapters and articles on big graphs querying and mining in the ACM SIGMOD blog, Springer Handbook of Big Data Technologies, and in Springer Encyclopedia of Big Data Technologies. He was invited to give tutorials and talks across 10 countries, including in the National Institute of Informatics (NII) Shonan Meeting on "Graph Database Systems: Bridging Theory, Practice, and Engineering", 2018, Japan, Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data (APWebWAIM 2017), International Conference on Management of Data (COMAD 2016), and in the Dagstuhl Seminar on graph algorithms and systems, 2014 and 2019, Schloss Dagstuhl - Leibniz Center for Informatics, Germany. Dr Khan is serving as an associate editor of IEEE TKDE 2019-21 and the proceedings chair of EDBT 2020.

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