Efficient Analysis of Latent Spaces in Heterogeneous Networks

This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identif...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2024-12
Hauptverfasser: Tian, Yuang, Sun, Jiajin, He, Yinqiu
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Tian, Yuang
Sun, Jiajin
He, Yinqiu
description This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and heterogeneous latent vectors are established simultaneously. The analysis framework offers remarkable flexibility, accommodating various types of edge weights under exponential family distributions.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3140664234</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3140664234</sourcerecordid><originalsourceid>FETCH-proquest_journals_31406642343</originalsourceid><addsrcrecordid>eNqNjLEKwjAUAIMgWLT_EHAupHlpdHEQqXQQF91LKC-SWpKalyL-vQp-gNPBcdyMZRKgLLZKygXLiXohhNQbWVWQsV1tresc-sT33gwvcsSD5SeTvuoymg6JO88bTBjDDT2GifgZ0zPEO63Y3JqBMP9xydbH-npoijGGx4SU2j5M8fOlFkoltFYSFPxXvQFvPDfi</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3140664234</pqid></control><display><type>article</type><title>Efficient Analysis of Latent Spaces in Heterogeneous Networks</title><source>Free E- Journals</source><creator>Tian, Yuang ; Sun, Jiajin ; He, Yinqiu</creator><creatorcontrib>Tian, Yuang ; Sun, Jiajin ; He, Yinqiu</creatorcontrib><description>This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and heterogeneous latent vectors are established simultaneously. The analysis framework offers remarkable flexibility, accommodating various types of edge weights under exponential family distributions.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Error analysis ; Estimation ; Networks</subject><ispartof>arXiv.org, 2024-12</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Tian, Yuang</creatorcontrib><creatorcontrib>Sun, Jiajin</creatorcontrib><creatorcontrib>He, Yinqiu</creatorcontrib><title>Efficient Analysis of Latent Spaces in Heterogeneous Networks</title><title>arXiv.org</title><description>This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and heterogeneous latent vectors are established simultaneously. The analysis framework offers remarkable flexibility, accommodating various types of edge weights under exponential family distributions.</description><subject>Error analysis</subject><subject>Estimation</subject><subject>Networks</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjLEKwjAUAIMgWLT_EHAupHlpdHEQqXQQF91LKC-SWpKalyL-vQp-gNPBcdyMZRKgLLZKygXLiXohhNQbWVWQsV1tresc-sT33gwvcsSD5SeTvuoymg6JO88bTBjDDT2GifgZ0zPEO63Y3JqBMP9xydbH-npoijGGx4SU2j5M8fOlFkoltFYSFPxXvQFvPDfi</recordid><startdate>20241203</startdate><enddate>20241203</enddate><creator>Tian, Yuang</creator><creator>Sun, Jiajin</creator><creator>He, Yinqiu</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20241203</creationdate><title>Efficient Analysis of Latent Spaces in Heterogeneous Networks</title><author>Tian, Yuang ; Sun, Jiajin ; He, Yinqiu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_31406642343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Error analysis</topic><topic>Estimation</topic><topic>Networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Tian, Yuang</creatorcontrib><creatorcontrib>Sun, Jiajin</creatorcontrib><creatorcontrib>He, Yinqiu</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tian, Yuang</au><au>Sun, Jiajin</au><au>He, Yinqiu</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Efficient Analysis of Latent Spaces in Heterogeneous Networks</atitle><jtitle>arXiv.org</jtitle><date>2024-12-03</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and heterogeneous latent vectors are established simultaneously. The analysis framework offers remarkable flexibility, accommodating various types of edge weights under exponential family distributions.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2024-12
issn 2331-8422
language eng
recordid cdi_proquest_journals_3140664234
source Free E- Journals
subjects Error analysis
Estimation
Networks
title Efficient Analysis of Latent Spaces in Heterogeneous Networks
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T17%3A02%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Efficient%20Analysis%20of%20Latent%20Spaces%20in%20Heterogeneous%20Networks&rft.jtitle=arXiv.org&rft.au=Tian,%20Yuang&rft.date=2024-12-03&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3140664234%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3140664234&rft_id=info:pmid/&rfr_iscdi=true