NECL uses neighborhood-similarity graph compression as a preprocessing step to accelerate random-walk network embedding algorithms without reducing their effectiveness on classification tasks.
2.1 Preliminaries In this section, we briefly discuss the necessary preliminaries for our new meta-strategy for graph embedding
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Network Embedding: on Compression and Learning
NECL uses neighborhood-similarity graph compression as a preprocessing step to accelerate random-walk network embedding algorithms without reducing their effectiveness on classification tasks.