HONEM learns embeddings for higher-order networks capturing non-Markovian dependencies and outperforms baselines on node classification, reconstruction, link prediction, and visualization.
Dynamic network embeddings: From random walks to temporal random walks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
HONEM: Learning Embedding for Higher Order Networks
HONEM learns embeddings for higher-order networks capturing non-Markovian dependencies and outperforms baselines on node classification, reconstruction, link prediction, and visualization.