Controlling Homophily in Social Network Regression Analysis by Machine Learning

同性恋 计算机科学 回归分析 社会网络分析 机器学习 回归 社交网络(社会语言学) 人工智能 计量经济学 心理学 统计 数学 社会化媒体 社会心理学 万维网
作者
Xuanqi Liu,Ke‐Wei Huang
出处
期刊:Informs Journal on Computing
标识
DOI:10.1287/ijoc.2022.0287
摘要

Across social science disciplines, empirical studies related to social networks have become the most popular research subjects in recent years. A frequently examined topic within these studies is the estimation of peer influence while controlling for homophily effects. However, although researchers may have access to all observable homophily variables, there is scarce literature addressing latent homophily effects stemming from unobservable features. Recent endeavors have demonstrated the efficacy of node embeddings derived from network structure in controlling latent homophily. Inspired by the network embedding research, this study introduces two methods that integrate node embeddings to better control latent homophily, particularly the nonlinear latent homophily effect. The first method uses double machine learning in the partially linear regression literature to alleviate estimation bias. The second method estimates peer influence effects directly by a novel neural network model. Our experimentation results show that our approaches outperform existing estimators in reducing the omitted variable bias due to homophily effects in network regression models. Theoretical analysis of two new estimation methods is also provided in this paper. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This research is supported by the National Research Foundation, Singapore under its Industry Alignment Fund - Pre-positioning (IAF-PP) Funding Initiative [Grant A-0003504-02-00]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0287 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0287 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
NexusExplorer应助HOPE采纳,获得10
刚刚
1秒前
东晓发布了新的文献求助10
2秒前
Boren完成签到,获得积分10
2秒前
hanleiharry1发布了新的文献求助10
2秒前
天峰完成签到,获得积分10
2秒前
李健应助风趣的爆米花采纳,获得10
3秒前
FashionBoy应助无名采纳,获得10
4秒前
超级的丸子完成签到,获得积分10
5秒前
6秒前
隐形曼青应助murry123采纳,获得10
7秒前
ANG完成签到 ,获得积分10
7秒前
8秒前
李嘉欣发布了新的文献求助10
9秒前
9秒前
lascqy完成签到 ,获得积分10
10秒前
wbh发布了新的文献求助10
11秒前
JamesPei应助咚咚咚采纳,获得30
12秒前
小熊熊完成签到,获得积分10
13秒前
Tessa完成签到,获得积分10
13秒前
王心耳完成签到,获得积分10
14秒前
扁舟灬完成签到,获得积分10
14秒前
周婷发布了新的文献求助10
14秒前
14秒前
puff关注了科研通微信公众号
15秒前
15秒前
17秒前
稳重岩完成签到 ,获得积分10
19秒前
loski发布了新的文献求助10
20秒前
步一发布了新的文献求助10
20秒前
21秒前
21秒前
hanleiharry1发布了新的文献求助10
21秒前
21秒前
murry123发布了新的文献求助10
22秒前
痴情的寒云完成签到 ,获得积分10
22秒前
CAOHOU应助张wx_100采纳,获得10
23秒前
24秒前
ppg123应助NightGlow采纳,获得10
25秒前
25秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989390
求助须知:如何正确求助?哪些是违规求助? 3531487
关于积分的说明 11254109
捐赠科研通 3270153
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809174