Fairness of Information Flow in Social Networks

Social networks form a major parts of people’s lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define the Information Unfairness (IUF) metric, which quantifies inequality in access to information across protected groups. We then introduce MinIUF, an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved.

[1]  Tina Eliassi-Rad,et al.  Information Access Equality on Network Generative Models , 2021, SSRN Electronic Journal.

[2]  Kristina Lerman,et al.  Emergence of Structural Inequalities in Scientific Citation Networks , 2021, ArXiv.

[3]  S. Chiappa,et al.  Fairness in Machine Learning , 2020, INNSBDDL.

[4]  Suresh Venkatasubramanian,et al.  Clustering via Information Access in a Network , 2020, ArXiv.

[5]  Chelpa Lingam,et al.  A SI model for social media influencer maximization , 2019, Applied Computing and Informatics.

[6]  D. Peuquet,et al.  STAND: a spatio-temporal algorithm for network diffusion simulation , 2019, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation.

[7]  Federico Battiston,et al.  Diffusive behavior of multiplex networks , 2019, New Journal of Physics.

[8]  Danah Boyd,et al.  Gaps in Information Access in Social Networks? , 2019, WWW.

[9]  Eric Rice,et al.  Group-Fairness in Influence Maximization , 2019, IJCAI.

[10]  Desmond J. Higham,et al.  Non-Backtracking Alternating Walks , 2019, SIAM J. Appl. Math..

[11]  M. Strohmaier,et al.  Homophily influences ranking of minorities in social networks , 2018, Scientific Reports.

[12]  Lucía Santamaría,et al.  Comparison and benchmark of name-to-gender inference services , 2018, PeerJ Comput. Sci..

[13]  Enhong Chen,et al.  Study on Information Diffusion Analysis in Social Networks and Its Applications , 2018, Int. J. Autom. Comput..

[14]  Ana-Andreea Stoica,et al.  Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity , 2018, WWW.

[15]  Cindy E. Hauser,et al.  The gender gap in science: How long until women are equally represented? , 2018, PLoS biology.

[16]  Peter Grindrod,et al.  The Deformed Graph Laplacian and Its Applications to Network Centrality Analysis , 2018, SIAM J. Matrix Anal. Appl..

[17]  Aristides Gionis,et al.  Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship , 2018, WWW.

[18]  Gianlorenzo D'Angelo,et al.  Recommending links through influence maximization , 2017, Theor. Comput. Sci..

[19]  Brian G. Knight,et al.  Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter , 2014 .

[20]  Abhijit Banerjee,et al.  Gossip: Identifying Central Individuals in a Social Network , 2014, ArXiv.

[21]  Brian Vinter,et al.  An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

[22]  Michalis Faloutsos,et al.  Gelling, and melting, large graphs by edge manipulation , 2012, CIKM.

[23]  Jie Tang,et al.  ArnetMiner: extraction and mining of academic social networks , 2008, KDD.

[24]  K. Ferraro,et al.  Neighborhood Residential Segregation and Physical Health among Hispanic Americans: Good, Bad, or Benign?∗ , 2007, Journal of health and social behavior.

[25]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[27]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[28]  Chungmei Lee,et al.  Why Segregation Matters: Poverty and Educational Inequality , 2005 .

[29]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[30]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[32]  Tim Christiansen,et al.  Race, homophily, and purchase intentions and the Black consumer , 2000 .

[33]  Herminia Ibarra,et al.  Paving an alternative route : Gender differences in managerial networks , 1997 .

[34]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[35]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[36]  Hema Raghavan,et al.  On the Information Unfairness of Social Networks , 2020, SDM.

[37]  Christopher J. Riederer,et al.  Algorithmic Glass Ceiling in Social Networks , 2018 .

[38]  Davide Anguita,et al.  Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf , 2015, INNS Conference on Big Data.

[39]  L. Takac.  DATA ANALYSIS IN PUBLIC SOCIAL NETWORKS , 2012 .

[40]  Y. Saad.  Numerical Methods for Large Eigenvalue Problems , 2011 .

[41]  Jafar Adibi,et al.  The Enron Email Dataset Database Schema and Brief Statistical Report , 2004 .

[42]  Daniel J. Brass,et al.  Relationships and Unethical Behavior: A Social Network Perspective , 1998 .

[43]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[44]  Joshua A. Tucker,et al.  Is Online Political Communication More Than an Echo Chamber? , 2022 .