476 citations · 512 across the 5 of their papers we have counts for
8 papers
Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
Brody Kutt, Pralay Ramteke, Xavier Mignot +6
Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a…
Graph Representation Ensemble Learning
Palash Goyal, Di Huang, Sujit Rokka Chhetri +3
Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links, and classifying and recommending nodes. Most embedding metho…
Benchmarks for Graph Embedding Evaluation
Palash Goyal, Di Huang, Ankita Goswami +3
Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and indust…
Tracking Temporal Evolution of Graphs using Non-Timestamped Data
Sujit Rokka Chhetri, Palash Goyal, Arquimedes Canedo
Datasets to study the temporal evolution of graphs are scarce. To encourage the research of novel dynamic graph learning algorithms we introduce YoutubeGraph-Dyn (available at http…
Pykg2vec: A Python Library for Knowledge Graph Embedding
Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo +2
Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture…
DynamicGEM: A Library for Dynamic Graph Embedding Methods
Palash Goyal, Sujit Rokka Chhetri, Ninareh Mehrabi +2
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs. It consists of state-of-the-art algorithms for defining embeddings of nodes whose c…
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
Palash Goyal, Sujit Rokka Chhetri, Arquimedes Canedo
Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static…
Future Automation Engineering using Structural Graph Convolutional Neural Networks
Jiang Wan, Blake S. Pollard, Sujit Rokka Chhetri +3
The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs. Classifying and clustering subgraphs according…