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Scaling Attributed Network Embedding to Massive Graphs

Summary: PANE scales attributed network embedding to massive graphs with a novel random-walk objective, fast initialization, and multi-core parallelization. It achieves high accuracy on attribute inference, link prediction, and node classification, beating baselines on MAG (59M nodes, 0.98B edges, 2000 attributes) in ~12 hours on a single server. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12559
Venue
VLDB
Year
2021
Pagerank
6.7550628e-05
Overall Rank
3,803 | 73.55%
DOI
10.14778/3421424.3421430

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
278 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00029230623
1,474 Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank 2020 VLDB 0.00011825229
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