Reconstruction and Analysis of Massive Historical Social Networks

hadithDescription ( 2013-Current )

Historical documents offer a wealth of information about the past including social linkages between people. The aim of this project is to use ancient and medieval texts in various languages to reconstruct social networks. The first set of texts that I am looking at consists of narrative texts (Hadith texts) in Arabic from the early history of Islam. By using natural language processing and crowd-sourcing I have been able to reconstruct a social network of more than 10,000 people from the first two centuries of Islamic history.

 Publications

  • Muhammad Aurangzeb Ahmad Towards the Analysis of Narrative Networks CSE Technical Report 13-017 Department of Computer Science, University of Minnesota May 23, 2013 [Link]
  • Muhammad Aurangzeb Ahmad Information Network Analysis meets Islamic Studies: Case study from the Analysis of the Hadith Literature Society for the Scientific Study of Religion Annual Conference. Boston, MA November 7, 2013

Project Website

The Hadith Networks Project

Simulations of Deceased People

Description ( 2013-Current )

The death of my father had a profound effect on me. It made me realize that my children will never have the opportunity to interact with him. This has prompted me to start work on this project where the aim is to create simulations of deceased people so that the living can interact with them. Right now this is all over the map, sitting in a place where machine learning, natural language processing, psychology and sociology meet.

Publications

  • Muhammad Aurangzeb Ahmad, After Death: Big Data and the Promise of Resurrection by Proxy Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2016. [Link]

Project Website

Mushtaq Ahmad Mirza Project


Data Mining Pilgrimage Data

Description ( 2016 )

Every year millions of Muslims go to the Holy cities of Mecca and Medina to perform the pilgrimage of Hajj. Over the course of just two weeks more than 3 million people are housed within an area less the size of Manhattan. Routing and providing services such a large mass of people is a logistical challenge that can be addressed via the application of machine learning to Big Data.

Computational Models of Trust in Massive Online Games

Description ( 2008-2011 )

Trust is a ubiquitous phenomenon in human societies. Computational trust refers to the mediation of trust via a computational infrastructure. It has been studied in a variety of contexts e.g., peer-to-peer systems, multi-agent systems, recommendation systems etc. While this is an active area of research, the types of questions that have been explored in this field has been limited mainly because of limitations in the types of datasets which are available to researchers. In this thesis questions related to trust in complex social environments represented by Massively Multiplayer Online Games (MMOGs) are explored. The main emphasis is that trust is a multi-level phenomenon both in terms of how it operates at multiple levels of network granularities and how trust relates to other social phenomenon like homophily, expertise, mentoring, clandestine behaviors etc. Social contexts and social environments affect not just the qualitative aspects of trust but this phenomenon is also manifested with respect to the network and structural signatures of trust network. Additionally trust is also explored in the context of predictive tasks: Previously described prediction tasks like link prediction are studied in the context of trust within the context of the link prediction family of problems: Link formation, link breakage, change in links etc. Additionally we define and explore new trust-related prediction problems i.e., trust propensity prediction, trust prediction across networks which can be generalized to the inter-network link prediction problem and success prediction based on using network measures of a person’s social capital as a proxy.

PhD Thesis

  • Muhammad Aurangzeb Ahmad (2012). Computational trust in Multiplayer Online Games.. University of Minnesota [Link].

Publications

  • Zoheb Hassan Borbora, Muhammad Aurangzeb Ahmad, Jehwan Oh, Karen Zita Haigh, Jaideep Srivastava, Zhen Wen Robust Features of Trust in Social Networks Social Network Analysis and Data Mining December 2013, Volume 3, Issue 4, pp 981-999 [Link]
  • Zoheb Borbora, Muhammad Aurangzeb Ahmad, Karen Zita Haigh, Jaideep Srivastava, Zhen Wen Exploration of Robust Features of Trust Across Multiple Social Networks. SASO Workshops 2011: 27-32 [Link]
  • Muhammad Aurangzeb Ahmad, Iftekhar Ahmad, Jaideep Srivastava, Marshall Poole Trust me, I ‘m an Expert: Trust, Homophily and Expertise in MMOs IEEE SocialCom 2011 Boston, MA October 9-11, 2011 [Link]
  • Young Ae Kim, Muhammad Aurangzeb Ahmad Trust, distrust and lack of confidence of users in online social media-sharing communities. Knowledge Based Systems 37: 438-450 (2013) [Link]
  • Muhammad Aurangzeb Ahmad, Marshall Scott Poole, Jaideep Srivastava The Trust Propensity Prediction Problem The Third ACM WebSci Conference, Koblenz, Germany June 14-17, 2011 [Presentation Video]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Trust Amongst Rogues? A Hypergraph Approach for Comparing Clandestine Trust Networks in MMOGs 5th International AAAI Conference on Weblogs and Social Media (ICWSM-11) [Link]
  • Muhammad Aurangzeb Ahmad, Marshall Scott Poole, Jaideep Srivastava, Network Exchange in Trust Networks IEEE Social Computing (SocialCom-10). Workshop on Social Intelligence in Applied Gaming. Minneapolis, MN, USA, August 20-22, 2010 [Link]
  • Young Ae Kim, Marla E. Eisenberg, Muhammad Aurangzeb Ahmad, Jaideep Srivastava Modeling Trust in Online Social Networks to Improve Adolescent Health Behavior, CSE Technical Report 10-018 Department of Computer Science, University of Minnesota August 18, 2010 [Link]
  • Young Ae Kim, Muhammad Aurangzeb Ahmad, Jaideep Srivastava, A Technique for Inferring Trust in Recommendation Systems International Sunbelt Social Network Conference (XXIX), San Diego, CA. March 14 2009

Press

Clandestine and Deviant Behaviors in Massive Online Games

Description ( 2009-2011 )

Gold Farming refers to a set of inter-related activities in online virtual spaces especially in Massively Multiplayer Games where certain players engage in repetitive activities to gain virtual commodities which they sell to other players. Game administrators actively ban Gold Farmers who have to hide their activities from the game admins, who are akin to law enforcers. In our research we compared Gold Farmers with their offline criminal counterparts. It was discovered that the behaviors of Gold Farmers is very similar to real world criminals in that their social networks tend to be quite similar.

Publications

  • Muhammad Aurangzeb Ahmad, Cuihua Shen, Jaideep Srivastava, Noshir Contractor (Editors) Predicting Real World Behaviors from Virtual World Data August 2014 Springer Verlog [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Atanu Roy, Dmitri Williams, Jaideep Srivastava, and Noshir Contractor. Guilt by association?: network based propagation approaches for gold farmer detection. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 121-126. ACM, 2013. [Link]
  • Atanu Roy, Muhammad Aurangzeb Ahmad, Chandrima Sarkar, Brian Keegan and Jaideep Srivastava The Ones That Got Away: False Negative Estimation Based Approaches for Gold Farmer Detection IEEE SocialCom 2012 Amsterdam, Netherlands September 3-5, 2012 [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Sophia Sullivan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor Illicit Bits: Detecting and Analyzing Contraband Networks in Massively Multiplayer Online Games IEEE SocialCom 2011 Boston, MA October 9-11, 2011 [Link]
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. Sic Transit Gloria Mundi Virtuali? Promise and Peril at the Intersection of Computational Social Science and Online Clandestine Organizations The Third ACM WebSci Conference, Koblenz, Germany June 14-17, 2011 (Best Paper Award) [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Trust Amongst Rogues? A Hypergraph Approach for Comparing Clandestine Trust Networks in MMOGs 5th International AAAI Conference on Weblogs and Social Media (ICWSM-11) [Link]
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Mapping Gold Farming Back to Offline Clandestine Organizations: Methodological, Theoretical, and Ethical Challenges. Game Behind the Game. (Best Paper Award)
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor, Dark Gold: Statistical Properties of Clandestine Networks in Massively-Muliplayer Online Games IEEE Social Computing Conference (SocialCom-10) Minneapolis, MN, USA, August 20-22, 2010 [Link]
  • Muhammad Aurangzeb Ahmad, Brain Keegan, Jaideep Srivastava, Dmitri Williams, Noshir Contractor, Mining for Gold Farmers: Automatic Detection of Deviant Players in MMOGS Proceedings of the 2009 IEEE Social Computing (SocialCom-09). Symposium on Social Intelligence and Networking (SIN-09). Vancouver, Canada, August 29-31, 2009 [Link]

Patent

  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor Automatic Detection of Deviant Players in MMORPGs Gold Farming (Patent Pending)

Press

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