In professional and other social settings, networks play an important role in people's lives. The communication between individuals and their positions in the network, may have a large impact on many aspects of their lives.In this work, I evaluate fairness from different perspectives.First,tomeasurefairnessfromgroupperspective,Iproposethenovelinformation unfairness criterion, which measures whether information spreads fairly to different groups in a network. Using this criterion, I perform a case study and measure fairness in information flow in different computer science co-authorship networks with respect to gender. Then, I consider two applications and show how to increase fairness with respect to a fairness metric. The first application is increasing fairness in information flow by adding a set of edges. I propose two algorithms- MaxFair and MinIUF- which are based on detecting those pairs of nodes whose connection would increase flow to disadvantaged groups. The second application is increasing fairness in organizational networks through employee hiring and assignment. I propose FairEA, a novel algorithm that allows organizations to gauge their success in achieving a diverse network. Next,Iexaminefairnessfromanindividualperspective.Iproposestratification assortativity, a novel metric that evaluates the tendency of the network to be divided into ordered classes. Then, I perform a case study on several co-authorship networks and examine the evolution of these networks over time and show that networks evolve into a highly stratified state. Finally, I introduce an agent-based model for network evolution to explain why social stratification emerges in a network.
","abstract_html":"<p>In professional and other social settings, networks play an important role in people's lives. The communication between individuals and their positions in the network, may have a large impact on many aspects of their lives.In this work, I evaluate fairness from different perspectives.First,tomeasurefairnessfromgroupperspective,Iproposethenovelinformation unfairness criterion, which measures whether information spreads fairly to different groups in a network. Using this criterion, I perform a case study and measure fairness in information flow in different computer science co-authorship networks with respect to gender. Then, I consider two applications and show how to increase fairness with respect to a fairness metric. The first application is increasing fairness in information flow by adding a set of edges. I propose two algorithms- MaxFair and MinIUF- which are based on detecting those pairs of nodes whose connection would increase flow to disadvantaged groups. The second application is increasing fairness in organizational networks through employee hiring and assignment. I propose FairEA, a novel algorithm that allows organizations to gauge their success in achieving a diverse network. Next,Iexaminefairnessfromanindividualperspective.Iproposestratification assortativity, a novel metric that evaluates the tendency of the network to be divided into ordered classes. Then, I perform a case study on several co-authorship networks and examine the evolution of these networks over time and show that networks evolve into a highly stratified state. Finally, I introduce an agent-based model for network evolution to explain why social stratification emerges in a network.</p>","abstract_has_math":false,"creators":["Saghati Jalali, Zeinab"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["Soundarajan, Sucheta"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05-15T07:00:00Z","date_published":"2022-05-15T07:00:00Z","updated_at":"2026-07-24T04:56:14Z","subjects":["Assignment","Fairness","Information Flow","Social Networks","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/etd/1471","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Soundarajan, Sucheta"]},{"key":"dc:creator","label":"Author","values":["Saghati Jalali, Zeinab"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Assignment","Fairness","Information Flow","Social Networks","Computer Sciences","Physical Sciences and Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/etd/1471"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In professional and other social settings, networks play an important role in people's lives. The communication between individuals and their positions in the network, may have a large impact on many aspects of their lives.In this work, I evaluate fairness from different perspectives.First,tomeasurefairnessfromgroupperspective,Iproposethenovelinformation unfairness criterion, which measures whether information spreads fairly to different groups in a network. Using this criterion, I perform a case study and measure fairness in information flow in different computer science co-authorship networks with respect to gender. Then, I consider two applications and show how to increase fairness with respect to a fairness metric. The first application is increasing fairness in information flow by adding a set of edges. I propose two algorithms- MaxFair and MinIUF- which are based on detecting those pairs of nodes whose connection would increase flow to disadvantaged groups. The second application is increasing fairness in organizational networks through employee hiring and assignment. I propose FairEA, a novel algorithm that allows organizations to gauge their success in achieving a diverse network. Next,Iexaminefairnessfromanindividualperspective.Iproposestratification assortativity, a novel metric that evaluates the tendency of the network to be divided into ordered classes. Then, I perform a case study on several co-authorship networks and examine the evolution of these networks over time and show that networks evolve into a highly stratified state. Finally, I introduce an agent-based model for network evolution to explain why social stratification emerges in a network.
"]},{"key":"dc:title","label":"Title","values":["Fairness in Social Networks"]}]}],"canonical_facts":{"dc:contributor":["Soundarajan, Sucheta"],"dc:creator":["Saghati Jalali, Zeinab"],"dc:description.abstract":["In professional and other social settings, networks play an important role in people's lives. The communication between individuals and their positions in the network, may have a large impact on many aspects of their lives.In this work, I evaluate fairness from different perspectives.First,tomeasurefairnessfromgroupperspective,Iproposethenovelinformation unfairness criterion, which measures whether information spreads fairly to different groups in a network. Using this criterion, I perform a case study and measure fairness in information flow in different computer science co-authorship networks with respect to gender. Then, I consider two applications and show how to increase fairness with respect to a fairness metric. The first application is increasing fairness in information flow by adding a set of edges. I propose two algorithms- MaxFair and MinIUF- which are based on detecting those pairs of nodes whose connection would increase flow to disadvantaged groups. The second application is increasing fairness in organizational networks through employee hiring and assignment. I propose FairEA, a novel algorithm that allows organizations to gauge their success in achieving a diverse network. Next,Iexaminefairnessfromanindividualperspective.Iproposestratification assortativity, a novel metric that evaluates the tendency of the network to be divided into ordered classes. Then, I perform a case study on several co-authorship networks and examine the evolution of these networks over time and show that networks evolve into a highly stratified state. Finally, I introduce an agent-based model for network evolution to explain why social stratification emerges in a network.
"],"dc:identifier":["https://surface.syr.edu/etd/1471"],"dc:subject":["Assignment","Fairness","Information Flow","Social Networks","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["Fairness in Social Networks"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:56:14Z"}