Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings-Reference-Cited by-同舟云学术

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Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings

Published:2023-01-01 Issue:1 Volume:10 Page:346-359
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Lin Christy¹^ORCID,Sussman Daniel L.²^ORCID,Ishwar Prakash³^ORCID

Affiliation:

1. Division of Systems Engineering, College of Engineering, Boston University, Boston, MA, USA

2. Department of Mathematics and Statistics, College of Arts and Sciences, Boston University, Boston, MA, USA

3. Department of Electrical and Computer Engineering and Division of Systems Engineering, College of Engineering, Boston University, Boston, MA, USA

Funder

National Science Foundation

Department of Electrical and Computer Engineering

Division of Systems Engineering at Boston University

Air Force Research Laboratory

Defense Advanced Research Projects Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/6488902/10009001/9896992-aam.pdf

Reference52 articles.

1. Spectral partitioning of graphs with general degrees and the extended planted partition model;chaudhuri;Proc 25th Conf Learn Theory,0

2. Statistical inference on random dot product graphs: A survey;athreya;J Mach Learn Res,2017

3. Network Embedding as Matrix Factorization

4. On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs

5. Effective algorithms with circulant-block matrices

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1. Performance‐driven contractor recommendation system using a weighted activity–contractor network;Computer-Aided Civil and Infrastructure Engineering;2024-08-29

2. A Theoretical Analysis of DeepWalk and Node2vec for Exact Recovery of Community Structures in Stochastic Blockmodels;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-02

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