328 citations · 1.2k across the 57 of their papers we have counts for
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Accurate, Efficient and Scalable Graph Embedding
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge…
GPOP: A cache- and work-efficient framework for Graph Processing Over Partitions
Kartik Lakhotia, Sourav Pati, Rajgopal Kannan +1
Past decade has seen the development of many shared-memory graph processing frameworks, intended to reduce the effort of developing high performance parallel applications. However…
Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding
Muhammad Rizwan Saeed, Charalampos Chelmis, Viktor K. Prasanna
Knowledge Graphs (KGs) are becoming essential to information systems that require access to structured data. Several approaches have been recently proposed, for obtaining vector re…