Journal article
Mutual Information Measure for Glass Ceiling Effect in Preferential Attachment Models
IEEE transactions on computational social systems, Vol.11(6), pp.7778-7788
12/2024
DOI: 10.1109/TCSS.2024.3432600
Abstract
This article introduces a novel mutual information-based measure to assess the glass ceiling effect in preferential attachment networks, which advances the analysis of inequalities in attributed networks. Using Shannon entropy and generalizing to Rényi entropy, our measure evaluates the conditional probability distributions of node attributes given the node degrees of adjacent nodes, which offers a more nuanced understanding of inequality compared to traditional methods that emphasize node degree distributions and degree assortativity alone. To evaluate the efficacy of the proposed measure, we evaluate it using an analytical structural inequality model as well as historical publication data. Results show that our mutual information measure aligns well with both the theoretical model and empirical data, underscoring its reliability as a robust approach for capturing inequalities in attributed networks. Moreover, we introduce a novel stochastic optimization algorithm that utilizes a parameterized conditional logit model for edge addition. Our algorithm is shown to outperform the baseline uniform distribution based approach in mitigating the glass ceiling effect. By strategically recommending links based on this algorithm, we can effectively hinder the glass ceiling effect within networks.
Details
- Title: Subtitle
- Mutual Information Measure for Glass Ceiling Effect in Preferential Attachment Models
- Creators
- Rui Luo - City University of Hong KongBuddhika Nettasinghe - University of IowaVikram Krishnamurthy - Cornell University
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on computational social systems, Vol.11(6), pp.7778-7788
- Publisher
- IEEE
- DOI
- 10.1109/TCSS.2024.3432600
- ISSN
- 2329-924X
- eISSN
- 2373-7476
- Grant note
- U.S. Army Research Office: W911NF-21-1-0093 National Science Foundation: CCF-2112457 City University of Hong Kong: 9610639
This work was supported in part by the U.S. Army Research Office under Grant W911NF-21-1-0093, in part by the National Science Foundation under Grant CCF-2112457, and in part by the City University of Hong Kong under Grant 9610639.
- Language
- English
- Electronic publication date
- 08/07/2024
- Date published
- 12/2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984696779702771
Metrics
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