FACT/metadata.yaml at main · Thiggel/FACT · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
# To be filled by the author(s) at the time of submission
# -------------------------------------------------------

# Title of the article:
#  - For a successful replication, it should be prefixed with "[Re]"
#  - For a failed replication, it should be prefixed with "[¬Re]"
#  - For other article types, no instruction (but please, not too long)
title: "[Re] Learning Fair Graph Representations via Automated Data Augmentations"

# List of authors with name, orcid number, email and affiliation
# Affiliation "*" means contact author (required even for single-authored papers)
# To include author suffix format name as Last Suffix, First M.I. (e.g. Schackart III, Kenneth E.)
authors:
  - name: Max Belitsky
    orcid: -
    email: max.belitsky@student.uva.nl
    affiliations: 1

  - name: Filipe Laitenberger
    orcid: -
    email: filipe.laitenberger@student.uva.nl
    affiliations: 1

  - name: Denys Sheremet
    orcid: -
    email: denys.sheremet@student.uva.nl
    affiliations: 1

  - name: Nordin Belkacemi
    orcid: -
    email: nordin.belkacemi@student.uva.nl
    affiliations: 1

# List of affiliations with code (corresponding to author affiliations), name
# and address. You can also use these affiliations to add text such as "Equal
# contributions" as name (with no address).
affiliations:
  - code:    1
    name:    University of Amsterdam
    address: Amsterdam, Netherlands

# List of keywords (adding the programming language might be a good idea)
keywords: rescience c, rescience x, python, graph, deep learning, geometric deep learning

# Code URL and DOI/SWH (url is mandatory for replication, doi after acceptance)
# You can get a DOI for your code from Zenodo, or an SWH identifier from
# Software Heritage.
#   see https://guides.github.com/activities/citable-code/
code:
  - url: https://zenodo.org/records/13834566
  - doi: 10.5281/zenodo.13834566
  - swh: -

# Data URL and DOI (optional if no data)
data:
  - url: https://zenodo.org/records/13837423
  - doi: 10.5281/zenodo.13837423

# Information about the original article that has been replicated
replication:
 - cite: Hongyi Ling, Zhimeng Jiang, Youzhi Luo, Shuiwang Ji, and Na Zou. Learning fair graph representations via automated data augmentations. In The Eleventh International Conference on Learning Representations, 2023
 - bib:  ling2023learning
 - url:  https://openreview.net/pdf?id=1_OGWcP1s9w
 - doi:  No DOI available

# Don't forget to surround abstract with double quotes
abstract: "We consider fair graph representation learning via data augmentations. While
this direction has been explored previously, existing methods invariably rely on
certain assumptions on the properties of fair graph data in order to design fxed
strategies on data augmentations. Nevertheless, the exact properties of fair graph
data may vary signifcantly in different scenarios. Hence, heuristically designed
augmentations may not always generate fair graph data in different application
scenarios. In this work, we propose a method, known as Graphair, to learn fair representations based on automated graph data augmentations. Such fairness-aware
augmentations are themselves learned from data. Our Graphair is designed to automatically discover fairness-aware augmentations from input graphs in order to
circumvent sensitive information while preserving other useful information. Experimental results demonstrate that our Graphair consistently outperforms many
baselines on multiple node classifcation datasets in terms of fairness-accuracy
trade-off performance. In addition, results indicate that Graphair can automatically learn to generate fair graph data without prior knowledge on fairness-relevant
graph properties. Our code is publicly available as part of the DIG package
(https://github.com/divelab/DIG)."

# Bibliography file (yours)
bibliography: main.bib

# Type of the article
# Type can be:
#  * Editorial
#  * Letter
#  * Replication
type: Replication

# Scientific domain of the article (e.g. Computational Neuroscience)
#  (one domain only & try to be not overly specific)
domain: Artificial Intelligence

# Coding language (main one only if several)
language: Python


# To be filled by the author(s) after acceptance
# -----------------------------------------------------------------------------

# For example, the URL of the GitHub issue where review actually occured
review:
  - url:

contributors:
  - name:
    orcid:
    role: editor
  - name:
    orcid:
    role: reviewer
  - name:
    orcid:
    role: reviewer

# This information will be provided by the editor
dates:
  - received:  November 1, 2018
  - accepted:
  - published:

# This information will be provided by the editor
article:
  - number: # Article number will be automatically assigned during publication
  - doi:    # DOI from Zenodo
  - url:    # Final PDF URL (Zenodo or rescience website?)

# This information will be provided by the editor
journal:
  - name:   "ReScience C"
  - issn:   2430-3658
  - volume: 4
  - issue:  1