40 citations · 56 across the 8 of their papers we have counts for
8 papers · 1 filter
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…
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…
Discovering Signals from Web Sources to Predict Cyber Attacks
Palash Goyal, KSM Tozammel Hossain, Ashok Deb +5
Cyber attacks are growing in frequency and severity. Over the past year alone we have witnessed massive data breaches that stole personal information of millions of people and wide…
DynGEM: Deep Embedding Method for Dynamic Graphs
Palash Goyal, Nitin Kamra, Xinran He +1
Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node clas…