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Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank

Summary: Proposes Homogeneous Network Embedding (HNE) for massive graphs via Node-Reweighted PageRank (NRP); adds node-degree reweighting to PPR to produce fixed-dim embeddings. O(m log n) time, O(m) space; beats 18 baselines on 7 real graphs for link prediction, reconstruction, and classification; on 1B-edge Twitter graph, ~4 hours on one CPU. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12265
Venue
VLDB
Year
2020
Pagerank
0.00011825229
Overall Rank
1,474 | 89.75%
DOI
10.14778/3377369.3377376

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