1 code implementation • 17 Nov 2022 • Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman, Nandini Ramanan, Sujit Rokka Chhetri, Shan Huang, Min Du, William Hewlett
CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario.
1 code implementation • 6 Sep 2019 • Palash Goyal, Di Huang, Sujit Rokka Chhetri, Arquimedes Canedo, Jaya Shree, Evan Patterson
In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.
1 code implementation • 19 Aug 2019 • Palash Goyal, Di Huang, Ankita Goswami, Sujit Rokka Chhetri, Arquimedes Canedo, Emilio Ferrara
We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.
1 code implementation • 4 Jul 2019 • Sujit Rokka Chhetri, Palash Goyal, Arquimedes Canedo
Datasets to study the temporal evolution of graphs are scarce.
Social and Information Networks
1 code implementation • 4 Jun 2019 • Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque
Python library for knowledge graph embedding and representation learning.
1 code implementation • 26 Nov 2018 • Palash Goyal, Sujit Rokka Chhetri, Ninareh Mehrabi, Emilio Ferrara, Arquimedes Canedo
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
1 code implementation • 7 Sep 2018 • Palash Goyal, Sujit Rokka Chhetri, Arquimedes Canedo
Capturing such evolution is key to predicting the properties of unseen networks.
Ranked #5 on Dynamic Link Prediction on Enron Emails
no code implementations • 24 Aug 2018 • Jiang Wan, Blake S. Pollard, Sujit Rokka Chhetri, Palash Goyal, Mohammad Abdullah Al Faruque, Arquimedes Canedo
The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs.