33.1k citations · 35.3k across the 95 of their papers we have counts for
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Few-Shot Knowledge Graph Completion
Chuxu Zhang, Huaxiu Yao, Chao Huang +3
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instance…
Graph Few-shot Learning via Knowledge Transfer
Huaxiu Yao, Chuxu Zhang, Ying Wei +5
Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest r…
HONEM: Learning Embedding for Higher Order Networks
Mandana Saebi, Giovanni Luca Ciampaglia, Lance M Kaplan +1
Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in…
Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements
Tianwen Jiang, Tong Zhao, Bing Qin +3
Conditions are essential in the statements of biological literature. Without the conditions (e.g., environment, equipment) that were precisely specified, the facts (e.g., observati…
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz +2
Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on…
LNEMLC: Label Network Embeddings for Multi-Label Classification
Piotr Szymański, Tomasz Kajdanowicz, Nitesh Chawla
Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multi-label classification focus on effective adaptation or transformat…