Laboratory of Social Analytics and Network Science (LSANS)

Welcome to LSANS!!

Our Laboratory focuses on advancing social analytics and network science, specifically focusing on the critical aspects of Fairness, Robustness, and Diversification within complex modern social and informational networks.

The overarching goal of any social or network algorithm is to extract meaningful insights or optimize dissemination while preserving computational performance and guaranteeing ethical constraints. Therefore, the deep analysis, modeling, and evaluation of social networks are essential for responsible technology design, effective policy-making, data privacy, and mitigating societal bias. Thus, many diverse topics such as algorithm auditing, resilient system architecture, and bias detection in automated systems are all subjects of investigation.

However, current work in the lab is towards

Building appropriate Influence Maximization models that ensure equitable spread across different communities and demographics.
Designing Link Prediction algorithms that account for inherent fairness constraints and minimize systemic biases.
Developing Community Detection methods that are robust against adversarial attacks and network noise.
Analyzing Information Dissemination to understand and counteract the spread of misinformation or echo chambers.
Developing scalable Heuristic and Meta-heuristic Algorithms (e.g., evolutionary computing, swarm intelligence) to optimize computationally expensive NP-hard problems like influence maximization in multi-million node networks.
Applying Soft Computing Techniques, including fuzzy logic, machine learning, and AI, to effectively model the inherent ambiguity, noise, and semantic uncertainty found in human-generated social media data.
Harnessing Advanced Graph Theory Concepts such as spectral graph theory, hypergraphs, and graph motifs to capture higher-order, multi-way interactions that traditional edge-node pairs fail to represent.
Exploring Quantum Computing and Quantum Walk Algorithms to accelerate complex network analytics, specifically targeting quantum-enhanced community detection and graph isomorphism challenges.
Designing approximation algorithms that integrate Societal Considerations, explicitly optimizing for algorithmic equity, transparency, and user privacy while mitigating the formation of digital echo chambers and polarization.

Current Lab Members

S. No.	Lab Member	Thesis Title	Institute	Course	Status	Role	Association
1.	Kirti Shukla	Advancing Influence Maximization in Signed Social Networks through Diversity and Diffusion Dynamics	South Asian University, New Delhi	Ph.D.	Ongoing	Supervisor	-
2.	Zubin Walia	Privacy Preserving Machine Learning	South Asian University, New Delhi	M.Tech.	Ongoing	Supervisor	Dr. Reshma Rastogi, South Asian University, New Delhi (Co- Supervisor)
3.	Pratima Rajput	Influence Maximization in Signed Social Networks	South Asian University, New Delhi	IMP	Ongoing	Supervisor	-
4.	Vithul Rajeev	Link Prediction in Signed Social Networks	South Asian University, New Delhi	M.Sc.	Ongoing	Supervisor	-

Graduated Lab Members

S. No.	Lab Member	Thesis Title	Institute	Completion Year	Role	Association
1.	Dr. Sunil Kumar Meena (Ph.D.)	Advancing Influence Maximization in Social Networks through Diversity and Diffusion Dynamics	University of Delhi, Delhi	2025	Co-Supervisor	Dr. Kuldeep Singh, University of Delhi, Delhi (Supervisor)

S. No.	Title Of Research Project	Sponsored Agency	Overall Budget	Duration	Status	Role	Association
1.	Algorithmic Aspects of Maximizing Content Spread in Social Networks for Diverse Applications	ANRF	INR 33,11,337/-	05-06-2026 To 04-06-2029	Ongoing	PI	-

S. No.	Title Of Research Project	Sponsored Agency	Overall Budget	Duration	Status	Role	Association
1.	FDP on Workshop on Social Network Analysis	E&ICT Academy, PDPM IIITDM Jabalpur	INR 4,86,500/-	25-05-2026 To 30-05-2026	Completed	Coordinator	Dr. Ashwini B. (SAU) Co-Coordinator