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https://olympias.lib.uoi.gr/jspui/handle/123456789/37053Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Manolis, Konstantinos | en |
| dc.date.accessioned | 2024-03-22T09:03:11Z | - |
| dc.date.available | 2024-03-22T09:03:11Z | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/37053 | - |
| dc.identifier.uri | http://dx.doi.org/10.26268/heal.uoi.16764 | - |
| dc.rights | CC0 1.0 Universal | * |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
| dc.subject | Social Networs, Community Detection, Community Fairness | en |
| dc.title | Modularity-Based Fairness in Network Communities | en |
| dc.type | masterThesis | en |
| heal.type | masterThesis | el |
| heal.type.en | Master thesis | en |
| heal.type.el | Μεταπτυχιακή εργασία | el |
| heal.secondaryTitle | Modularity-Based Fairness in Network Communities | en |
| heal.classification | Social Networks | - |
| heal.identifier.secondary | Social Networks | el |
| heal.dateAvailable | 2024-03-22T09:04:12Z | - |
| heal.language | en | el |
| heal.access | free | el |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Πολυτεχνική Σχολή | el |
| heal.publicationDate | 2024-02-23 | - |
| heal.abstract | In this thesis, we study the fairness of community structures in networks from a group-based perspective. Specifically, we assume that individuals in a social network belong to different groups based on the value of one of their sensitive attributes, such as their age, gender, or race. We view community fairness as the lack of discrimination towards any of the groups. For simplicity, let us assume that nodes belong to two groups, the blue and the red group. We introduce three fairness metrics. The first metric, termed balance-fairness, equitably represents communities by ensuring an equal distribution of red and blue nodes in each community. The second, termed modularity-fairness, refines the notion of modularity to demand equal intracommunity connectivity for the groups. The third metric, termed diversity-fairness, promotes intra-community edges between nodes of different color thus addressing the filter-bubble phenomenon. We have modified the Louvain algorithm, a well-known community detection algorithm, to produce communities that are both well-connected and fair. We present an extensive evaluation using several real-world and synthetic networks. The goal of our evaluation is twofold: (1) to study the fairness of communities in networks and the causes of unfairness and (2) to evaluate the effectiveness of our fairness-enhanced Louvain algorithm. | en |
| heal.advisorName | Pitoura, Evaggelia | en |
| heal.committeeMemberName | Lykas, Aristidis | en |
| heal.committeeMemberName | Tsaparas, Panagiotis | en |
| heal.academicPublisher | Πανεπιστήμιο Ιωαννίνων. Πολυτεχνική Σχολή. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.academicPublisherID | uoi | el |
| heal.fullTextAvailability | true | - |
| Appears in Collections: | Διατριβές Μεταπτυχιακής Έρευνας (Masters) - ΜΗΥΠ | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Social_Networks_Thesis.pdf | 1.3 MB | Adobe PDF | View/Open | |
| Social_Networks_Thesis.pdf | 1.3 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License
