Research Interest

My research is in the area of large-scale graph analytics. Currently, I am working on the Heatwave project at the Oracle Labs. In my Ph.D. thesis, I studied the problem of Influence Maximization in Social Networks. During my internship at the Huawei Big Data Lab, I worked on Knowledge Graphs. I have also interned with the Amazon Research team, where I worked on item recommendations for shared accounts.

Before my Ph.D., I worked in the IBM Research Lab for three years. There I focused on solving route recommendation problems on road networks.

Selected Publications

More information is available in my google scholar profile

2020

• Prithu Banerjee, Wei Chen and Laks V.S. Lakshmanan. "Maximizing Social Welfare in a Competitive Diffusion Model", VLDB 2021.[full paper in arxiv] [conference paper] [code] [talk slides] [talk video]

• Prithu Banerjee, Lingyang Chu, Yong Zhang, Laks V.S. Lakshmanan and Lanjun Wang. "Stealthy Targeted Data Poisoning Attack on Knowledge Graphs", ICDE 2021.

2019

• Prithu Banerjee, Wei Chen and Laks V.S. Lakshmanan. "Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model ", Proceedings of the 2019 International Conference on Management of Data (SIGMOD) 2019.[full paper in arxiv] [conference paper] [code] [talk slides] [talk video]

2016

• Prithu Banerjee, Pranali Yawalkar and Sayan Ranu. "MANTRA: A Scalable Approach to Mining Temporally Anomalous Sub-trajectories", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2016.[paper] [code]

2014

• Prithu Banerjee, Sayan Ranu and Sriram Raghavan. "Inferring Uncertain Trajectories from Partial Observations", IEEE International Conference on Data Mining (ICDM) 2014.[paper] [code]

Services

• Served as a program committee member at the following recent conferences KDD 2022 (research track), VLDB 2022, EDBT 2023, AIMLSystems 2022