publications

arranged in reverse chronological order

Here is a link to my Google Scholar page and one to my OpenReview profile.

2024

Cause and effect: Can large language models truly understand causality?

Swagata Ashwani, Kshiteesh Hegde, Nishith Reddy Mannuru, and 6 more authors

In Proceedings of the AAAI Symposium Series, 2024

2019

Network Lens: Node Classification in Topologically Heterogeneous Networks

Kshiteesh Hegde, and Malik Magdon-Ismail

arXiv preprint arXiv:1901.09681, 2019
The intrinsic scale of networks is small

Malik Magdon-Ismail, and Kshiteesh Hegde

In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2019

2018

Separating Terrorist-Like Topological Signatures Embedded in Benign Networks

Kshiteesh Hegde, and Malik Magdon-Ismail

In MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), 2018
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification

Kshiteesh Hegde, Malik Magdon-Ismail, Ram Ramanathan, and 1 more author

2018

2017

Clustering, Prominence and Social Network Analysis on Incomplete Networks

Kshiteesh Hegde, Malik Magdon-Ismail, Boleslaw Szymanski, and 1 more author

In Complex Networks & Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016), 2017

2016

Recommendations For Streaming Data

Karthik Subbian, Charu Aggarwal, and Kshiteesh Hegde

In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016

Abs

Recommender systems have become increasingly popular in recent years because of the broader popularity of many web-enabled electronic commerce applications. However, most recommender systems today are designed in the context of an offline setting. The online setting is, however, much more challenging because the existing methods do not work very effectively for very large-scale systems. In many applications, it is desirable to provide real-time recommendations in large-scale scenarios. The main problem in applying streaming algorithms for recommendations is that the in-core storage space for memory-resident operations is quite limited. In this paper, we present a probabilistic neighborhood-based algorithm for performing recommendations in real-time. We present experimental results, which show the effectiveness of our approach in comparison to state-of-the-art methods.