Thematic Session at NetSci-X 2026

The Algorithmic Fairness in Network Science (FairNetSci) thematic session explores the biases and disparities within networks and their implications on algorithmic outcomes. Network inequality refers to the structural biases, perception disparities, and persistent inequalities stemming from connection patterns among agents in a network. These biases shape individuals' social views, behavioural decisions, and influence, highlighting the real-world impact of network structures.

Algorithms designed without addressing such biases risk producing unfair outcomes, particularly for minority groups. For example, link prediction algorithms may fail to accurately predict connections for smaller or less connected groups due to structural biases. Furthermore, recommendation systems can inadvertently amplify existing inequalities through their learning processes. However, with thoughtful design, algorithms can mitigate these biases and promote fair outcomes for all individuals and groups, irrespective of their size or type.

This thematic session aims to bring together experts from diverse fields, including computer science, data science, social science, mathematics, and network science, to collaboratively explore and address these challenges. The satellite will feature Keynote, invited, and contributed lightning talks.

This satellite event also allows junior researchers to showcase their work, including graduate students, postdocs, and young professors. A Best Student Paper Award will be presented to the most outstanding submission led by a graduate student.

Call for Abstracts

Join us in the beautiful city of Auckland for the “Algorithmic Fairness in Network Science” thematic session at the NetSciX-26 event. This session aims to bring together researchers and practitioners from diverse fields to explore inequality in network structures and dynamics, as well as the development of fair and ethical algorithms in network science.

Submissions from junior researchers, including graduate students, postdocs, and pre-tenure faculty, are especially encouraged. The submissions will be accepted for regular talks, lightning talks, and poster presentations.

Scope

We invite submissions on understanding network inequalities and designing fair algorithms in network science. Contributions are welcome on, but not limited to, the following topics:

Structuring Inequalities in Networks
Measuring and mitigating biases in network algorithms
Fairness in SNA methods, such as community detection, link prediction, and influence maximisation
Disparities in access, visibility, or resources in social and information networks
Algorithmic fairness in recommender systems and social network platforms
Case studies on the real-world impact of inequalities in networks
Ethical considerations in the design and deployment of network algorithms
Strategies for ensuring inclusivity and equity in network-driven decision-making

This session does not have archival proceedings. Submissions may include:

Recently published works
Papers under peer review (e.g., arXiv papers)
Work in progress

Submission Round one deadline: November 15, 2025 .

Submission

Submission Format

Option 1 — Preprint/Publication Submission: Submit a PDF of your publication or preprint. This option is straightforward and ideal for authors without copyright restrictions or concerns about sharing their paper/preprint on another platform.

Option 2 — Abstract Submission: For abstract submission, we recommend 1 to 3 pages, with unlimited pages for references and figures, but longer submissions are also accepted. You may use any template of your choice.

Submission Portal

Enter your submission using the following Google Form: https://forms.gle/JhqTDhS5PsM6CQ4P6

Important Dates

We will have two submission rounds. To take advantage of NetSciX’s early bird registration rate, please submit in Round 1. Decisions for Round 1 will be communicated by November 20, 2026.

November 1: Call for abstracts is released
November 15: Round 1 deadline
November 20: Round 1 acceptance notice
November 30: Early bird registration deadline (NetSciX 2026 Registration)
November 25: Round 2 deadline (dependent on the number of accepted papers from Round 1)
November 30: Round 2 acceptance notice

Submissions will be evaluated for contributed talks based on their relevance to the satellite theme, originality, novelty, and scientific quality. Notification of acceptance will be sent by November 20 and November 30, 2025. Upon acceptance, at least one author must register and attend the session to present the work.

If you have any queries, please email us at fairnetsci@gmail.com.

Speakers

TBA

Program

TBA

Organizers

Dr. Akrati Saxena

Dr.Akrati Saxena is an assistant professor at LIACS, the computer science and AI institute of the Faculty of Science of Leiden University. She leads the AlFa (Algorithmic Fairness) research group, which develops fairness-aware heuristic, approximation, machine learning, and deep learning-based methods for complex network data. Her research interest lies at the intersection of Social Network Analysis, Complex Networks, Computational Social Science, Data Science, and Algorithmic Fairness. Her current work focuses on understanding inequalities in complex networks and advancing fairness-aware algorithms in network and data science, including analyzing biases in existing systems, defining fairness constraints and evaluation metrics, and designing fair computational frameworks. In addition to her research, she serves on the Diversity Committee at LIACS, contributing to efforts that foster inclusion and equity within the academic community.

Dr. Frank Takes

Dr. Frank Takes is an associate professor at LIACS, the computer science and AI institute of the Faculty of Science of Leiden University. His research interest is in network science, in particular, the development of algorithms for the analysis of large-scale (social) network data, with applications in science studies, economics, and computational social science. He is the head of the Leiden Computational Network Science research group, academic co-director of the Dutch PLANET-NL platform for population-scale social network analysis, and board member of the Dutch Network Science Society. Since 2022, he has been the director of education of the LIACS bachelor programmes Informatica (Computer Science) and Data Science & Artificial Intelligence, and a member of the institute's management team.