Hello!! I am Amit Goyal, a final year PhD student in Department of Computer Science
in University of British Columbia
(UBC). My supervisor is Dr.
Laks V. S. Lakshmanan. I completed my Bachelors in Computer Science from Indian Institute of Technology,
Bombay (IITB). My PhD thesis focus on understanding
Influence propagation in Social Networks and how it can be
leveraged in various applications like Viral Marketing.
Research Interests:
1. Social Influence and its Applications
2. Recommender Systems
3. Social Networks Analysis
4. Data Mining
4. Algorithms
PhD Thesis: Here
Publications:
Wei Lu, Xiaokui Xiao, Amit Goyal, Keke Huang, Laks V. S. Lakshmanan, Refutations on "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study". Tech Report, 2017. (Paper, arXiv, Slides)
Wei Lu, Francesco Bonchi, Amit Goyal, Laks V. S. Lakshmanan, The Bang for the Buck: Fair Competitive Viral Marketing from the Host Perspective. In KDD, 2013. (Paper)
Amit Goyal, Laks V. S. Lakshmanan, RecMax: Exploiting Recommender Systems for Fun and Profit. In KDD, 2012. (Paper, Presentation, Poster)
Smriti Bhagat, Amit Goyal, Laks V. S. Lakshmanan, Maximizing Product Adoption in Social Networks. In WSDM 2012. (Paper, Presentation)
Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan, A Data-Based approach to Social Influence Maximization. In PVLDB 2012. (Paper, Presentation, Source Code)
Amit Goyal, Wei Lu, Laks V. S. Lakshmanan, Simpath: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model. In ICDM 2011. (Paper, Presentation, Source Code)
Amit Goyal, Wei Lu, Laks V. S. Lakshmanan, CELF++: Optimizing the Greedy Algorithm for Influence Maximization in Social Networks. In WWW 2011 (Companion Volume). (Edited Version, Original Paper, Source Code)
[CELF vs CELF++ Note (May 2017)]: It has come to our attention that the results in this poster paper may not be statistically significant, thereby proving the claim that CELF++ is decisively faster than CELF as incorrect. We reimplemented the code (source here) and reran our experiments. In particular, we observed that there is non-trivial variance in running times in both CELF and CELF++. This variance increases as we try to run multiple experiments at the same time. Therefore, to obtain a statistically significant result, it is required that only one experiment is run at a time (even if the machine has multiple cores, as in many CPU designs, the L2 caches may be shared), and each experiment is run multiple times to get a distribution of running times. A statistical significance test can then be applied. We acknowledge that results reported in this paper ran into noise and are not statistically significant. We would like to thank Akhil Arora, Sainyam Galhotra and Sayan Ranu to bring up the issue to our attention. ]
Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan, Learning Influence Probabilities in Social Networks. In Proc. of the 3rd ACM International Conference on Web Search and Data Mining, WSDM 2010, New York City, 2010 (Paper, Presentation)
Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan, Discovering Leaders from Community Actions. In Proc. of the 17th Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, 2008 (Paper, Presentation)
Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan, Suresh Venkatasubramanian, On Minimizing Budget and Time in Influence Propagation over Social Networks. In Social Network Analysis and Mining, 2012. (Paper)
Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan, Byung-Won On, Gurumine: a Pattern Mining System for Discovering Leaders and Tribes. In Proc. of the 25th Intl. Conference on Data Engineering, ICDE 2009, Shanghai, China, 2009 (Demo Paper) (PDF)
Email:
goyal4u[AT]gmail[DOT]com, goyal[AT]cs[DOT]ubc[DOT]ca