Dr. Akrati Saxena
Research Fellow, Eindhoven University of Technology

IBAE Young Achiever Award 2019, Google WTM Scholar 2017, GHCI18 Scholar, HLF18 Young Researcher, GYSS19 Young Scientist

Research Interest: Data Science, Complex Networks, Social Network Analysis, Computational Social Science, Machine Learning, Social Media, Fairness

Reviewer/PC Member: Data Mining and Knowledge Discovery, Applied Network Science, IEEE Transactions on Network Science and Engineering, GeoInformatica, Post-ASONAM Edited Book (2016), International Journal of Modern Physics B, International Journal of Data Science and Analysis, Data Science Track @ GHC 2019, Poster Track @ GHC 2019, CSS Summer School on Methods for analyzing and modeling multimedia data 2019, TAPIA Conference 2019, RESPECT 2020, CSoNet 2019, Networks and Education track @ HUSO 2019

In preparation / To be submitted

  • Akrati Saxena, Yulong Pei, George Fletcher, and Mykola Pechenizkiy. Anomaly detection in banking transactions.
  • Akrati Saxena, George Fletcher, and Mykola Pechenizkiy. Fairness-aware Influence Blocking in Social Networks.
  • Ricky Fajri, Akrati Saxena, Yulong Pei, and Mykola Pechenizkiy. FairBAL : Fair Batch-Mode Active Learning for Algorithmic Decision Making.
  • Rasmus Helles, Akrati Saxena, Jakob Grue Simonsen, Zhan Su, and Antti Veilahti. Tracking Analysis of Educational Websites based on large-scale Web Crawl. (authors in alphabetical order)
  • Ralucca Gera, Akrati Saxena, D'Marie Bartolf, Simona Tick. A Network Science Approach to Personalized Education.

Under Review

  • Afrizal Doewes, Akrati Saxena, Yulong Pei, and Mykola Pechenizkiy. Evaluating Fairness in Automated Essay Scoring Systems. (Submitted to EDM Conference)
  • Rrubaa Panchendrarajan, and Akrati Saxena. Topic-based Influential User Detection : A Survey. (Submitted to Applied Intelligence Journal)
  • Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, and Mykola Pechenizkiy. Survey on Fairness Approaches in Reinforcement Learning : Theory and Practice. (Submitted to FACCT Conference)
  • Akrati Saxena, George Fletcher, and Mykola Pechenizkiy. Fairness-aware Methods in Network Science. (Submitted to FACCT Conference)

Book Chapters

  • Saxena A. (2022) Evolving Models for Dynamic Weighted Complex Networks. In: Biswas A., Patgiri R., Biswas B. (eds) Principles of Social Networking. Smart Innovation, Systems and Technologies, vol 246. Springer, Singapore. pdf
  • Saxena A., Reddy H., Saxena P. (2022) Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and Applications. In: Biswas A., Patgiri R., Biswas B. (eds) Principles of Social Networking. Smart Innovation, Systems and Technologies, vol 246. Springer, Singapore. pdf
  • Saxena A., Reddy H., Saxena P. (2022) Recent Developments in Sentiment Analysis on Social Networks: Techniques, Datasets, and Open Issues. In: Biswas A., Patgiri R., Biswas B. (eds) Principles of Social Networking. Smart Innovation, Systems and Technologies, vol 246. Springer, Singapore. pdf
  • Saxena A., Saxena P., Reddy H. (2022) Fake News Detection Techniques for Social Media. In: Biswas A., Patgiri R., Biswas B. (eds) Principles of Social Networking. Smart Innovation, Systems and Technologies, vol 246. Springer, Singapore. pdf
  • Saxena A., Saxena P., Reddy H. (2022) Fake News Propagation and Mitigation Techniques: A Survey. In: Biswas A., Patgiri R., Biswas B. (eds) Principles of Social Networking. Smart Innovation, Systems and Technologies, vol 246. Springer, Singapore. pdf
  • Aikta Arya, Pradumn Kumar Pandey, and Akrati Saxena. Node Classification in Complex Networks using Deep Learning. (Accepted in Deep Learning for Social Media Data Analytics, Studies in Big Data, Springer book series)*
  • Rrubaa Panchendrarajan, and Akrati Saxena. Deep Learning for Analyzing Code-Mixed Text in Social Media. (Accepted in Deep Learning for Social Media Data Analytics, Studies in Big Data, Springer book series)*
  • Toshita Sharma, Rrubaa Panchendrarajan, and Akrati Saxena. Characterisation of Mental Health Conditions in Social Media using Deep Learning Techniques. (Accepted in Deep Learning for Social Media Data Analytics, Studies in Big Data, Springer book series)*
* The Deep Learning for Social Media Data Analytics book will be available online in June 2022.

Journals

  • Rrubaa Panchendrarajan, and Akrati Saxena. Topic-based Influential User Detection : A Survey. (Accepted in Applied Intelligence Journal)
  • Saxena, A., Fletcher, G. and Pechenizkiy, M., 2022. Nodesim: Node similarity based network embedding for diverse link prediction. EPJ Data Science, 11(1), pp.1-22.
  • Miller, R., Saxena, A., & Gera, R., A community-guided approach for dark network disruption. Military Operations Research Journal, 2022.
  • Saxena, A., Fletcher, G., & Pechenizkiy, M. (2021). HM-EIICT: Fairness-aware link prediction in complex networks using community information. Journal of Combinatorial Optimization, 1-18.
  • Saxena, A., & Reddy, H. (2021). Users roles identification on online crowdsourced Q&A platforms and encyclopedias: a survey. Journal of Computational Social Science, 1-33.
  • Saxena, A., Gera, R., Bermudez, I., Cleven, D., Kiser, E. T., & Newlin, T. (2019). Twitter Response to Munich July 2016 Attack: Network Analysis of Influence. Frontiers in Big Data, 2, 17.
  • Saxena, A., Gera, R., & Iyengar, S. R. S. (2019). A heuristic approach to estimate nodes’ closeness rank using the properties of real world networks. Social Network Analysis and Mining, 9(1), 1-16.
  • Gupta, Y., Iyengar, S. R. S., Saxena, A., & Das, D. (2019). Modeling memetics using edge diversity. Social Network Analysis and Mining, 9(1), 1-17.
  • Saxena, A., Gera, R., & Iyengar, S. R. S. (2018). Estimating degree rank in complex networks. Social Network Analysis and Mining, 8(1), 1-20.

Conferences

  • Ralucca Gera, D'Marie Bartolf, Simona Tick, and Akrati Saxena. CHUNK Learning: A Tool that Supports Personalized Education. (Demo paper accepted in EDM Conference 2022)
  • Doewes, A., Saxena, A., Pei, Y., & Pechenizkiy, M. Individual Fairness Evaluation for Automated Essay Scoring System. Accepted in EDM Conference 2022.
  • Maneet Singh, Akrati Saxena, Sudarshan Iyengar, and Rishemjit Kaur. A Bi-level Assessment of Twitter Data versus Election Results: Delhi Assembly Elections 2020. Accepted in TempWeb Workshop, The Web Conference 2022.
  • Saxena, A., Pei, Y., Veldsink, J., van Ipenburg, W., Fletcher, G., & Pechenizkiy, M. (2021). The Banking Transactions Dataset and its Comparative Analysis with Scale-free Networks. Accepted in ASONAM.
  • Malhotra, D., Gera, R., & Saxena, A. (2021, November). Community Detection Using Semilocal Topological Features and Label Propagation Algorithm. In International Conference on Computational Data and Social Networks (pp. 255-266). Springer, Cham.
  • Saxena, A., Fletcher, G., & Pechenizkiy, M. (2021, April). How Fair is Fairness-aware Representative Ranking?. In Companion Proceedings of the Web Conference 2021 (pp. 161-165).
  • Saxena, A., Saxena, H., & Gera, R. (2020, December). k-TruthScore: Fake News Mitigation in the Presence of Strong User Bias. In International Conference on Computational Data and Social Networks (pp. 113-126). Springer, Cham.
  • Saxena, A., Hsu, W., Lee, M. L., Leong Chieu, H., Ng, L., & Teow, L. N. (2020, April). Mitigating Misinformation in Online Social Network with Top-k Debunkers and Evolving User Opinions. In Companion Proceedings of the Web Conference 2020 (pp. 363-370).
  • Saxena, A., Saxena P., Reddy H, & Gera R. (2019) A Survey on Studying the Social Networks of Students. In The Fifth International Conference on Human and Social Analytics (pp 50-57).
  • Saxena, A., & Iyengar, S. R. S. (2018, December). K-Shell Rank Analysis Using Local Information. In International Conference on Computational Social Networks (pp. 198-210). Springer, Cham.
  • Miller, R., Gera, R., Saxena, A., & Chakraborty, T. (2018, August). Discovering and leveraging communities in dark multi-layered networks for network disruption. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1152-1159). IEEE.
  • Saxena, A., & Iyengar, S. R. S. Global Rank Estimation in Complex Networks. ICDCN 2018
  • Saxena, A., Gera, R., & Iyengar, S. R. S. (2017, July). Fast estimation of closeness centrality ranking. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 80-85).
  • Saxena, A., Gera, R., & Iyengar, S. R. S. (2017, July). Observe locally rank globally. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 139-144).
  • Gera, R., Miller, R., MirandaLopez, M., Saxena, A., & Warnke, S. (2017, July). Three is the answer: Combining relationships to analyze multilayered terrorist networks. In 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 868-875). IEEE. (authors in alphabetical order)
  • Adeniji, O., Cohick, D. S., Gera, R., Castro, V. G., & Saxena, A. (2017, July). A Generative Model for the Layers of Terrorist Networks. In 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 690-697). IEEE. (authors in alphabetical order)
  • Saxena, A., & Iyengar, S. R. S. (2016, January). Evolving models for meso-scale structures. In 2016 8th International Conference on Communication Systems and Networks (COMSNETS) (pp. 1-8). IEEE.
  • Saxena, A., Malik, V., & Iyengar, S. R. S. (2016, January). Estimating the degree centrality ranking. In 2016 8th International Conference on Communication Systems and Networks (COMSNETS) (pp. 1-2). IEEE.
  • Gupta, Y., Saxena, A., Das, D., & Iyengar, S. R. S. (2016). Modeling memetics using edge diversity. In Complex Networks VII (pp. 187-198). Springer, Cham.
  • Saxena, A., Iyengar, S. R. S., & Gupta, Y. (2015, August). Understanding spreading patterns on social networks based on network topology. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (pp. 1616-1617).

Technical Reports

  • Gera, R., Saxena, A., Bartolf, D. M., & Tick, S. (2021). A Network Science Perspective to Personalized Learning. arXiv preprint arXiv:2111.01321.
  • Saxena, A., & Iyengar, Sudarshan. (2020). Centrality Measures in Complex Networks: A Survey. arXiv preprint arXiv:2011.07190.
  • Saxena, Akrati, Jaspal Singh Saini, Yayati Gupta, Aishwarya Parasuram, Neeharika, S.R.S. Iyengar. Social Network Analysis of the Caste-Based Reservation System in India. arXiv preprint arXiv:1512.03184.
  • Saxena, Akrati, Vaibhav Malik, and S. R. S. Iyengar. "Estimating the Degree Centrality Ranking of a Node." arXiv preprint arXiv:1511.05732 (2015).
  • Saxena, Akrati, Vaibhav Malik, and S. R. S. Iyengar. "Rank me thou shalln't Compare me." arXiv preprint arXiv:1511.09050 (2015).
  • Saxena, Akrati, et al. "Modeling Memetics using Edge Diversity." arXiv preprint arXiv:1505.00457 (2015).


  Privacy Lost: How regional differences in power and politics have shaped worldwide digital surveillance

The Privacy Lost project uses web tracking (based on cookies and scripts embedded in websites) as a key exemplar of digital surveillance. The main aim of the project is to understand how the dynamics of tracking have been shaped by variations in economic, social and cultural factors across the world, and how this in turn sheds light on the development of surveillance capitalism. We analyze the historical data of web pages from multiple geographic and cultural regionsusing network science, information retrieval, and natural language processing, to highlight the core elements and dynamics of digital surveillance visible.

  Tool-assisted Personalized Learning Methodology: Chunk Learning

What is the potential of a 21st century learning environment that mirrors the capabilities of personalized Apps? In contrast to the standard linear or tree-like educational system of sequential lectures or chapters, we design a real-time, modular, adaptive teaching-learning environment for enhanced and personalized education, called the Curated Heuristic Using a Network of Knowledge (CHUNK) Learning concept. The CHUNK Learning model breaks away from the predictable pattern of traditional education models and provides content delivery that adapts to the capabilities, learning styles, and approaches to problem-solving for every learner. The CHUNK Learning tool is a student-centered teaching-learning system whose purpose is to make learning engaged, flexible, and respectful of the students' time. This system converts curricula into a network composed of nodes, where each node is referred to as a CHUNK of educational content and edges capture relationships that exist among the nodes. The Network of Knowledge is composed of lesson materials joined together by prerequisite relationships and common attributes based on competency or skill levels. Our main goal is how we can develop a personalized, streamlined path for learners engaged in e-learning to make interdisciplinary learning easy for students belonging to different fields. We are also working on a recommender system to recommend the most relevant CHUNKs to a learner based on the last CHUNK that they have completed and their learning goals.

  How Fair is Fairness-aware Representative Ranking

It has been observed in several works that the ranking of candidates based on their score can be biased for candidates belonging to the minority community. In recent works, the fairness-aware representative ranking was proposed for computing fairness-aware re-ranking of results. The proposed algorithm achieves the desired distribution of top-ranked results with respect to one or more protected attributes. In this project, we highlight the bias in fairness-aware representative ranking for an individual and for a group if the group is sub-active on the platform. We define individual unfairness and group unfairness from two different perspectives. We aim to propose methods to generate ideal individual and group fair representative ranking if the universal representation ratio is known.

  Fairness-aware Link Prediction

In real-world complex networks, understanding the dynamics of their evolution has been of great interest to the scientific community. Predicting future links is an essential task of social network analysis as the addition or removal of the links over time leads to the network evolution. In this project, we aim to propose methods for fair link prediction. The fainress will be verified using modularity reduction and fainress metrics.

  Fake News Mitigation Techniques

Today each online social network hosts millions of user accounts. These social networks provide an easy platform to share the information where most of the information is shared as a microblog. Due to the easy sharing of information, the spread of fake news and rumors has been prevalent. We have seen the impact of spreading of fake news on major events like the US election, Jakarta election, or distorting the reputation of a company. In this project, we focus on proposing the mitigation techniques using network science-based approach. We also reviewed the existing fake news detection and mitigation techniques, and what kind of actions can be taken further to control the fake news spreading.

  Estimate Global Rank of a Node using Local Information

In real-world complex networks, the importance of a node depends on two important parameters: 1. characteristics of the node, and 2. the context of the given application. The current literature contains several centrality measures that have been defined to measure the importance of a node based on the given application requirements. In this project, we aim to propose fast and efficient methods to estimate the global centrality rank of a node without computing the centrality value of all the nodes. These methods are further extended to estimate the rank without having the entire network. The proposed methods are based on the structural behavior of centrality measures, network properties, and sampling techniques. We have proposed methods to estimate the degree, closeness, and k-shell rank of the nodes. My Ph.D. thesis is based on this project.

  Social Network Analysis of the Caste-Based Reservation System in India

Being as old as human civilization, discrimination based on various grounds as race, creed, gender, and caste is prevailing in the world from a long time. To undo the impact of this long-enduring historical discrimination, governments worldwide have adopted various forms of affirmative action; such as positive discrimination, employment equity, and quota system. Locally known as “Reservation” policy, affirmative action in India is one of the world's oldest and most complex affirmative policy. Although being one of the most controversial and frequently debated issues, the reservation system in India lacks a rigorous scientific study and analysis. In this paper, we discuss the dynamics of the reservation system based on the cultural divide among Indian population using social network analysis. The mathematical model, using Erdos-Renyi network, shows that the addition of weak ties between the two components leads to a logarithmic reduction in the social distance. Our experimental simulations establish the claim for the different clans of frequently studied social network models as well as real-world networks. We further show that a small number of links created by the process of reservation are adequate for a society to live in harmony.

  Gender Bias and Affirmative Actions

For the past several decades, gender-based biases have been prevalent in society. The gender ratio bias is also present among the people working in STEM. For the past few decades, several govt, organizations, private institutions, NGOs, etc., have worked extensively to reduce the gender gap in STEM. Still, we are far from reaching an equal gender ratio. In this project, we focus on the analysis and modeling of biases in the communities based on gender, race, etc. and the impact of affirmative actions to remove these biases from society. We work towards modeling of biases, actions for removing them, influence propagation, and the emergence of leaders in such scenarios. Apart from this, we also study gender bias in online learning platforms. How different people acquire different roles for the stability of the ecosystem and how they converge over time. This project opens a wide range of questions that are yet to be explored and answered.

  Compare the Evolving Phenomenon of Real-world Networks

Decision makers use partial information networks to guide their decision, yet when they act, they act in the real network or the ground truth. Therefore, a way of comparing the partial information to ground truth is required. In this project, we aim to propose methods for comparing the evolution process of real-world networks. We introduced a statistical measure that analyzes the network obtained from the partially observed information and ground truth, which of course can be applied to the comparison of any networks. As a first step, in the current research, we restrict ourselves to networks of the same size to introduce such a method, which can be generalized to different size networks. Next, we focus on generalizing the proposed method to compare the network of different sizes.

  Evolving Models for Meso-scale Structures

Real-world scale-free networks possess both the community as well as the core-periphery meso-scale structures that shows the modular and hierarchical organization in the given networks. This project mainly focuses on understanding the evolving phenomenon and coexistence of core-periphery community structures. Based on our observations, we further propose evolving models to generate synthetic weighted and unweighted scale-free networks having both meso-scale structures.

  Analysis of Dark Multilayered Networks

In this project, we present a synthesized analysis of three terrorist networks through the analysis of the multiple layers of these networks. First, we study how these networks have different characteristics than scale-free real-world networks. The main challenges associated with these networks are incompleteness, fuzzy boundaries, and dynamic behavior. We account these characteristics and propose a method to identify knowledge sharing communities (KSC). We also proposed models to generate multilayered synthetic networks having similar properties.

  Coreness Approximation using Local Information

For network scientists, it has always been an interesting problem to identify the influential nodes in a given network. K-shell decomposition method is a widely used method which assigns a shell-index value to each node based on its influential power. K-shell method requires the entire network to compute the shell-index of a node that is infeasible for large-scale real-world dynamic networks. In this project, we focus on estimating the shell-index of a node using local neighborhood information. Next, we use the estimated coreness value to estimate the global coreness rank of a node without having the entire network.

  Modeling Memetics using Edge Diversity

In this project, we study how does a meme spread on the network. We study real-world meme spreading datasets to observe the role of core nodes in making a meme viral. We further study the impact of core-periphery and community structure on the meme spreading. Based on our observations, we proposed a meme spreading model using penta-level classification of edges in the network. The proposed spreading model is verified using Twitter datasets.


Experience:

  • Eindhoven University of Technology

    Research Fellow | March 2020 to Present | Netherlands

  • National University of Singapore

    Postdoctoral Fellow | November 2018 to March 2020 | Singapore

  • Indian Institute of Technology Ropar, India

    Research Scholar | July 2014 to October 2018 | Punjab, India

  • State Bank Of India

    IT Officer (Assistant Manager) | August 2013 to July 2014 | Patiala, Punjab, India

  • Newgen Software Technology Ltd.

    Software Developer | July 2011 to August 2013 | New Delhi, India

  • Tulip Telecom Delhi

    Summer Intern | May 2010 to July 2010 | New Delhi India

Lecturer/Instructor:

  • JM0150, Data Mining, co-taught with Dr. Joaquin Vanschoren (Fall 2021, Data Science and Entrepreneurship master at JADS, Eindhoven University of Technology, Netherlands)
  • 2IMM00, Seminar Data Mining course, (Q2, Nov 2021 - Jan 2022, Eindhoven University of Technology, Netherlands)
  • JBP000 Final Bachelor Project, (Aug 2021 - Jan 2022, Eindhoven University of Technology, Netherlands)

Teaching Assistantships:

  • JBG040, Data Challenge 1 with Dr. Maryam Tavakol (Q2, Nov 2021 - Jan 2022, Eindhoven University of Technology, Netherlands)
  • GE103, Introduction to Programming and Data Structures with Dr. Nitin Auluck (Spring Semester, 2018, Indian Institute of Technology Ropar)
  • CSL720, Introduction to Spatial Computing with Dr. Venkata M. Viswanath Gunturi (Autumn Semester, 2018, Indian Institute of Technology Ropar)
  • CSL811, Special Topics in Social computing with Dr. S.R.S. Iyengar (Autumn Semester, 2018, Indian Institute of Technology Ropar)
  • CSL471, Introduction to Probability and Computing with Dr. S.R.S. Iyengar (Spring Semester, 2017, Indian Institute of Technology Ropar)
  • CSL343, Computer Networks with Dr. Junghyun Jun (Autumn Semester, 2017, Indian Institute of Technology Ropar)
  • CSL356, Analysis and Design of Algorithms with Dr. Junghyun Jun (Spring Semester, 2016, Indian Institute of Technology Ropar)
  • CSL343, Computer Networks with Dr. Junghyun Jun (Autumn Semester, 2016, Indian Institute of Technology Ropar)
  • CSL469, Wireless and Mobile Systems with Dr. Junghyun Jun (Spring Semester, 2015, Indian Institute of Technology Ropar)
  • CSL451, Introduction to Database Systems with Dr. Narayanan C. Krishnan (Autumn Semester, 2015, Indian Institute of Technology Ropar)
  • GEL103, Introduction to Computing with Dr. Nitin Auluck (Autumn Semester, 2015, Indian Institute of Technology Ropar)
  • CSL407, Machine Learning with Dr. Narayanan C. Krishnan (Spring Semester, 2014, Indian Institute of Technology Ropar)

Research Internships:

  • May 2015 - July 2015, Project: Community detection in multilayer networks, under Dr. Ralucca Gera, Associate Professor, NPS, California, USA
  • September 2015 - December 2015, Project: Community detection in multilayer networks, under Dr. Ralucca Gera, Associate Professor, NPS, California, USA
  • May 2016 - July 2016, Project: Analysis of multilayered terrorist networks, under Dr. Ralucca Gera, Associate Professor, NPS, California, USA
  • April 2017 - July 2017, Project: Study the properties of dark networks and propose evolution models to generate multilayered dark networks, under Dr. Ralucca Gera, Associate Professor, NPS, California, USA

Edited Books:

  Co-editing Deep Learning for Social Media Data Analytics book, Studies in Big Data, Springer book series. Editors: Prof. Dr. Tzung-Pei Hong, Dr. Leticia Serrano Estrada, Dr. Akrati Saxena, Dr. Anupam Biswas. (More details here).

Tutorials:

  • Roles Analytics in Networks - Foundations, Methods and Applications tutorial at ICDM 2021 conference (More details here)
  • Network Science Applications to Education in the 21st Century tutorial at ASONAM 2021 conference (More details here)

Special Tracks:

  • Role Acquisition and Modeling (RAM) at CSoNet 2019 conference (co-organized with Prof. Dr. Ralucca Gera)

Workshops:

  • Organizer at TED Your Ideas workshop on Technical Writing and Presentation skills, IIT Ropar, India, March 2018
  • Organizer at Social Network Analysis with Python workshop, AKTU University, Lucknow, India, February 2018

Research Collaborations:

  • University of Copenhagen, Denmark
  • DTU, Denmark
  • RMIT, Australia
  • Naval Postgraduate School, CA, USA
  • IIT Roorkee, India
  • IIT Ropar, India

Presentations and Talks

 "Fair Link Prediction Methods in Complex Networks", at Faculty Development Programme (FDP) on ‘Research Scopes in Data Science, Analysis, and Visualisation’ organized by PSG Center for Academic Research and Excellence, India, October 2021 (Invited Talk)

 "Link Prediction: Theory and Applications", at Faculty Development Programme (FDP) on ‘Network Science: Theory, Challenges and Applications’ organized by Amity University Rajasthan, Jaipur, India, August 2021 (Invited Talk)

 "NodeSim: Node Similarity based Network Embedding for Diverse Link Prediction", at FRCCS, May 2021

 "How fair is Fairness-aware Representative Ranking?", at DSSGW, The Web Conference, April 2021

 "k-TruthScore: Fake News Mitigation in the Presence of Strong User Bias", at CSoNet, December 2020

 "Mitigating Misinformation in Online Social Network with Top-k Debunkers and Evolving User Opinions", at The Web Conference, April 2020

 "K-shell Rank Analysis using Local Information", at CSoNet, Shanghai, China, December 2018

 "Fast and Efficient Methods to Estimate the Centrality Rank of the Nodes in Complex Networks", at IIT Ropar, India, August 2018

 "Fast and Efficient Methods to Estimate Global Rank of the Nodes using Network Characteristics", at IIT Ropar, India, May 2018

  "Global Rank Estimation in Complex Networks" in ICDCN 2018, IIT BHU, India, January 2018

  "Rank me, thou shalln’t compare me: Rank estimation in complex networks", at IIT Ropar, India, December 2017

  "Estimate Closeness Centrality Rank using its Structural Behavior", at IIT Ropar, India, September 2017

  "Rank me thou shan’t Compare me" at CSE department, IIT Ropar, India, October 2016

  "Evolving Models for Meso-Scale Structures" in Comsnets at Bangalore, India, January 2016.

  "Estimating the Degree Centrality Ranking" in Comsnets at Bangalore, India, January 2016.

  "Evolution of Core-Periphery Structure" in NAG15 international workshop held at IIT Ropar, December 2015 .

  "Evolution of Core-Periphery Structure in Complex networks" in Cynosure at IIT Ropar, November 2015

  "Predict the Degree Centrality Ranking" in Cynosure at IIT Ropar, November 2015

  "Evolving Phenomenon of Core in Real World Networks" at CSE Department IIT Ropar, August 2015

  "Synonymy Networks and Their Structure" at CSE Department IIT Ropar, January 2015

 "Virality Prediction in Online Social Networks: A Novel Approach" in TEQIP-II at PEC Chandigarh , November 2014


Co-supervised/Mentored 

Bachelor Students:

  Toshita Sharma, TU/e, Netherlands, Jan 2022- May 2022 (co-supervising bachelor thesis project).

  Giel Beuzel, TU/e, Netherlands, Aug 2021- Jan 2022 (co-supervised bachelor thesis).

  Deepanshu Malhotra, Intern, May 2021- Nov 2021 (co-supervised with Prof. Dr. Ralucca Gera).

  Supriti Vijay, Intern, Copenhagen University, Denmark, July 2021- Dec 2021.

  Sajid Hussain, Summer Intern, IIT Ropar, India, May 2021- July 2021.

  Ashbin Jaison, Summer Intern, IIT Ropar, India, May 2019- July 2019.

  Hima Sambath, Summer Intern, IIT Ropar, India, May 2019- July 2019.

  Richik Das, Summer Intern, IIT Ropar, India, May 2018- July 2018.

  Sharmila Dhayal, Summer Intern, IIT Ropar, India, May 2018- July 2018.

  Himanshu Beniwal, Summer Intern, IIT Ropar, India, May 2018- July 2018.

  Priyanshu Ranjan, B.Tech. project, IIT Ropar, July 2016- May 2017.

  Jagadeesh, B.Tech. project, IIT Ropar, July 2016- May 2017.

  Devendra Pratap Yadav, B.Tech. project, IIT Ropar, Jan 2017- Dec 2017.

  Jayant Bisht, Summer Intern, IIT Ropar, May 2017-July 2017.

  Harita Reddy, Summer Intern, IIT Ropar, May 2017-July 2017.

  Meghana Batchu, Summer Intern, IIT Ropar, May 2017-July 2017 .

  Sameer Arora, Summer Intern, IIT Ropar, May 2017-July 2017.

  Harsimran Singh, Summer Intern, IIT Ropar, May 2017-July 2017.

  Vardaan Bajaj, Summer Intern, IIT Ropar, May 2017-July 2017.

  Pushpendra Tiwari, Summer Intern, IIT Ropar, May 2016-July 2016.

  Pratik Chhajer, Summer Intern, IIT Ropar, May 2016-July 2016.

Master Students:

  C Gayathri, IIT Madras, India, Sep 2021- May 2022 (co-supervising Master thesis project).

  Prateek Munjal, IIT Ropar, India, July 2017- Dec 2017.

Ph.D. Students Mentored:

  Vaishali Kansal, IIT Roorkee, India.

  Aikta Arya, IIT Roorkee, India.

  Afrizal Doewes, TU/e Netherlands.

  Maneet Singh, IIT Ropar, India.

  Zhan Su, Copenhagen University, Denmark.

  Ruda Nie, RMIT, Australia.


Dr. Akrati Saxena
Research Fellow
Department of Mathematics and Computer Science,
Eindhoven University of Technology, Netherlands

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