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PANE: scalable and effective attributed network embedding

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Abstract

Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node \(v \in G\) to a compact vector \(X_v\), which can be used in downstream machine learning tasks. Ideally, \(X_v\) should capture node v’s affinity to each attribute, which considers not only v’s own attribute associations, but also those of its connected nodes along edges in G. It is challenging to obtain high-utility embeddings that enable accurate predictions; scaling effective ANE computation to massive graphs with millions of nodes pushes the difficulty of the problem to a whole new level. Existing solutions largely fail on such graphs, leading to prohibitive costs, low-quality embeddings, or both. This paper proposes \(\texttt {PANE}\), an effective and scalable approach to ANE computation for massive graphs that achieves state-of-the-art result quality on multiple benchmark datasets, measured by the accuracy of three common prediction tasks: attribute inference, link prediction, and node classification. \(\texttt {PANE}\) obtains high scalability and effectiveness through three main algorithmic designs. First, it formulates the learning objective based on a novel random walk model for attributed networks. The resulting optimization task is still challenging on large graphs. Second, \(\texttt {PANE}\) includes a highly efficient solver for the above optimization problem, whose key module is a carefully designed initialization of the embeddings, which drastically reduces the number of iterations required to converge. Finally, \(\texttt {PANE}\) utilizes multi-core CPUs through non-trivial parallelization of the above solver, which achieves scalability while retaining the high quality of the resulting embeddings. The performance of \(\texttt {PANE}\) depends upon the number of attributes in the input network. To handle large networks with numerous attributes, we further extend \(\texttt {PANE}\) to \(\texttt{PANE}^{++}\), which employs an effective attribute clustering technique. Extensive experiments, comparing 10 existing approaches on 8 real datasets, demonstrate that \(\texttt {PANE}\) and \(\texttt{PANE}^{++}\) consistently outperform all existing methods in terms of result quality, while being orders of magnitude faster.

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Notes

  1. The work reported here is an extended version of [92, 93].

  2. In the degenerate case that \(v_l\) is not associated with any attribute, e.g., \(v_1\) in Fig. 1, we simply restart the random walk from the source node \(v_i\) and repeat the process.

  3. The PMI quantifies how much more or less likely we are to see the two events co-occur, given their individual probabilities, and relative to the case where they are completely independent.

  4. http://linqs.soe.ucsc.edu/data

  5. https://github.com/mengzaiqiao/CAN

  6. http://snap.stanford.edu/data

  7. https://www.kaggle.com/c/kddcup2012-track1

  8. http://ma-graph.org/rdf-dumps/

  9. https://figshare.com/articles/dataset/mag_scholar/12696653

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Acknowledgements

This work is supported by the National University of Singapore SUG grant R-252-000-686-133, Singapore Government AcRF Tier-2 Grant MOE2019-T2-1-029, NPRP grant #NPRP10-0208-170408 from the Qatar National Research Fund (Qatar Foundation), and the financial support of Hong Kong RGC ECS (No. 25201221) and Start-up Fund (P0033898) by PolyU. The findings herein reflect the work, and are solely the responsibility of the authors.

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Correspondence to Sourav S. Bhowmick.

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Yang, R., Shi, J., Xiao, X. et al. PANE: scalable and effective attributed network embedding. The VLDB Journal 32, 1237–1262 (2023). https://doi.org/10.1007/s00778-023-00790-4

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