Arijit Khan, Synergy of Graph Data Management and Machine Learning in Explainability and Query Answering

2024, diiP, Distinguished Lectures, Events

Arijit Khan
June 5, 2024, at 4 PM
Online (Zoom)

Prof Arijit Khan
(Aalborg University, Denmark)

Arijit Khan is an Associate Professor at Aalborg University, Denmark. His PhD is from University of California, Santa Barbara, USA, and he did a post-doc in the Systems group at ETH Zurich, Switzerland.

He has been an assistant professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. His research is on data management and machine learning for the emerging problems in large graphs. He is an IEEE senior member and an ACM distinguished speaker. Dr Khan is the recipient of the IBM Ph.D. Fellowship (2012-13) and a VLDB Distinguished Reviewer award (2022). He is the author of a book on uncertain graphs and over 80 publications in top venues including ACM SIGMOD, VLDB, IEEE TKDE, IEEE ICDE, SIAM SDM, USENIX ATC, EDBT, The Web Conference (WWW), ACM WSDM, ACM CIKM, ACM TKDD, and ACM SIGMOD Record.

Dr Khan is serving as an associate editor of IEEE TKDE 2019-2024 and ACM TKDD 2023-now, proceedings chair of EDBT 2020, IEEE ICDE TKDE poster track co-chair 2023, and ACM CIKM short paper track co-chair 2024.

Abstract

Graph data, e.g., social and biological networks, financial transactions, knowledge graphs, and transportation systems are pervasive in the natural world, where nodes are entities with features, and edges denote relations among them. Machine learning and recently, graph neural networks become ubiquitous, e.g., in cheminformatics, bioinformatics, fraud detection, question answering, and recommendation over knowledge graphs.

In this talk, I shall introduce our ongoing works about the synergy of graph data management and graph machine learning in the context of graph neural network explainability and query answering. In the first direction, I shall discuss how data management techniques can assist in generating user‐friendly, configurable, queryable, and robust explanations for graph neural networks. In the second direction, I shall provide an overview of our user‐friendly, deep learning‐based, scalable techniques and systems for querying knowledge graphs.

 

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