Overcoming Bias in Community Detection Evaluation
- URL: http://arxiv.org/abs/2102.03472v1
- Date: Sat, 6 Feb 2021 01:53:51 GMT
- Title: Overcoming Bias in Community Detection Evaluation
- Authors: Jeancarlo Campos Le\~ao (1), Alberto H. F. Laender (2), Pedro O. S.
Vaz de Melo (2) ((1) Instituto Federal do Norte de Minas Gerais, (2)
Universidade Federal de Minas Gerais)
- Abstract summary: Two widely used strategies to assess this task are generally known as structural and functional.
We propose an approach that supports a robust quality evaluation when detecting communities in real-world networks.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Community detection is a key task to further understand the function and the
structure of complex networks. Therefore, a strategy used to assess this task
must be able to avoid biased and incorrect results that might invalidate
further analyses or applications that rely on such communities. Two widely used
strategies to assess this task are generally known as structural and
functional. The structural strategy basically consists in detecting and
assessing such communities by using multiple methods and structural metrics. On
the other hand, the functional strategy might be used when ground truth data
are available to assess the detected communities. However, the evaluation of
communities based on such strategies is usually done in experimental
configurations that are largely susceptible to biases, a situation that is
inherent to algorithms, metrics and network data used in this task.
Furthermore, such strategies are not systematically combined in a way that
allows for the identification and mitigation of bias in the algorithms, metrics
or network data to converge into more consistent results. In this context, the
main contribution of this article is an approach that supports a robust quality
evaluation when detecting communities in real-world networks. In our approach,
we measure the quality of a community by applying the structural and functional
strategies, and the combination of both, to obtain different pieces of
evidence. Then, we consider the divergences and the consensus among the pieces
of evidence to identify and overcome possible sources of bias in community
detection algorithms, evaluation metrics, and network data. Experiments
conducted with several real and synthetic networks provided results that show
the effectiveness of our approach to obtain more consistent conclusions about
the quality of the detected communities.
Related papers
- Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms [49.1574468325115]
This study employs the same method to evaluate the relevance of using local similarity metrics for community detection.
The efficacy of these metrics was evaluated by applying the base algorithm to several real networks with varying community sizes.
arXiv Detail & Related papers (2024-08-17T02:17:09Z) - A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups.
We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
arXiv Detail & Related papers (2024-01-26T14:21:45Z) - Implicit models, latent compression, intrinsic biases, and cheap lunches
in community detection [0.0]
Community detection aims to partition a network into clusters of nodes to summarize its large-scale structure.
Some community detection methods are inferential, explicitly deriving the clustering objective through a probabilistic generative model.
Other methods are descriptive, dividing a network according to an objective motivated by a particular application.
We present a solution that associates any community detection objective, inferential or descriptive, with its corresponding implicit network generative model.
arXiv Detail & Related papers (2022-10-17T15:38:41Z) - A Survey of Community Detection Approaches: From Statistical Modeling to
Deep Learning [95.27249880156256]
We develop and present a unified architecture of network community-finding methods.
We introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning.
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
arXiv Detail & Related papers (2021-01-03T02:32:45Z) - Detecci\'on de comunidades en redes: Algoritmos y aplicaciones [0.0]
This master's thesis work has the objective of performing an analysis of the methods for detecting communities in networks.
As an initial part, I study of the main features of graph theory and communities, as well as common measures in this problem.
I was performed a review of the main methods of detecting communities, developing a classification, taking into account its characteristics and computational complexity.
arXiv Detail & Related papers (2020-09-15T00:18:06Z) - On the use of local structural properties for improving the efficiency
of hierarchical community detection methods [77.34726150561087]
We study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection.
We also check the performance impact of network prunings as an ancillary tactic to make hierarchical community detection more efficient.
arXiv Detail & Related papers (2020-09-15T00:16:12Z) - Deep Learning for Community Detection: Progress, Challenges and
Opportunities [79.26787486888549]
Article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
This article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
arXiv Detail & Related papers (2020-05-17T11:22:11Z) - Certified Robustness of Community Detection against Adversarial
Structural Perturbation via Randomized Smoothing [81.71105567425275]
We develop the first certified robustness guarantee of community detection against adversarial structural perturbation.
We theoretically show that the smoothed community detection method provably groups a given arbitrary set of nodes into the same community.
We also empirically evaluate our method on multiple real-world graphs with ground truth communities.
arXiv Detail & Related papers (2020-02-09T18:39:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.