Debiasing Community Detection: The Importance of Lowly Connected Nodes
By Ninareh Mehrabi, Fred Morstatter, Nanyun Peng, Aram Galstyan. 2019
32 citations recorded by the source corpus.
Community detection is an important task in social network analysis, allowing us to identify and understand the communities within the social structures provided by the network. However, many community detection approaches either fail to assign low-degree (or lowly connected) users to communities, or assign them to trivially small communities that prevent them from being included in analysis. In this work we investigate how excluding these users can bias analysis results. We then introduce an approach that is more inclusive for lowly connected users by incorporating them into larger groups. Experiments show that our approach outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity scores while reducing the bias towards low-degree users.
Subfields: Information Systems, Statistical and Nonlinear Physics, Graph Learning, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Fred Morstatter
- Aram Galstyan
- Ninareh Mehrabi
- Nripsuta Ani Saxena
- Arjan Durresi
- Kristina Lerman
- Kaley J. Rittichier
- Suleyman Uslu
In Statistical and Nonlinear Physics
- Aram Galstyan
- Kristina Lerman
- Fred Morstatter
- Nanyun Peng
- Ninareh Mehrabi
- Emilio Ferrara
- Nazanin Alipourfard
- Peter G. Fennell
In Graph Learning
In AI Ethics, Fairness & Society
- Aram Galstyan
- Fred Morstatter
- Ninareh Mehrabi
- Arjan Durresi
- Kristina Lerman
- Kaley J. Rittichier
- Suleyman Uslu
- Nripsuta Ani Saxena
Citations in the corpus
- A Survey on Bias and Fairness in Machine Learning (2019, background): “In [85] authors show how social networks can be biased toward low-degree nodes when only”
- Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition (2019, background): “From representation learning (Moyer et al. 2018) to graph embedding (Bose and Hamilton 2019) to community detection (Mehrabi et al. 2019a) and clustering (Backurs et al. 2019), researchers have studied biases in these areas and tried to address them by pointing out the observed problems and…”
- Trustworthy Artificial Intelligence: A Review (2022, methodology): “Supervised Learning [33] Data Pre-Processing [63, 97, 128] Item-Set Mining [169] Dimensionality Reduction [172] Data/Model Bias Community Detection [135] Data Labeling [170]”