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Inward and Outward Network Influence Analysis

Measuring heterogeneous influence across nodes in a network is critical in network analysis. This article proposes an inward and outward network influence (IONI) model to assess nodal heterogeneity. Specifically, we allow for two types of influence parameters; one measures the magnitude of influence that each node exerts on others (outward influence), while we introduce a new parameter to quantify the receptivity of each node to being influenced by others (inward influence). Accordingly, these two types of influence measures naturally classify all nodes into four quadrants (high inward and high outward, low inward and high outward, low inward and low outward, and high inward and low outward). To demonstrate our four-quadrant clustering method in practice, we apply the quasi-maximum likelihood approach to estimate the influence parameters, and we show the asymptotic properties of the resulting estimators. In addition, score tests are proposed to examine the homogeneity of the two types of influence parameters. To improve the accuracy of inferences about nodal influences, we introduce a Bayesian information criterion that selects the optimal influence model. The usefulness of the IONI model and the four-quadrant clustering method is illustrated via simulation studies and an empirical example involving customer segmentation.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.40 No.4 October 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1617-1628

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MODEL SELECTION
QUASI-MAXIMUM LIKELIHOOD ESTIMATION
CUSTOMER SEGMENTATION
FOUR-QUADRANT CLUSTERING
INWARD AND OUTWARD NETWORK INFLUENCE MODEL

Info Detail Spesifik

https://doi.org/10.1080/07350015.2021.1953509

Pernyataan Tanggungjawab

Yujia Wu, Wei Lan, Tao Zou, Chih-Ling Tsai

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