Search Results for author: Talal Rahwan

Found 12 papers, 4 papers with code

AI-generated faces free from racial and gender stereotypes

1 code implementation1 Feb 2024 Nouar AlDahoul, Talal Rahwan, Yasir Zaki

Additionally, we examine the degree to which Stable Diffusion depicts individuals of the same race as being similar to one another.

Coupled-Space Attacks against Random-Walk-based Anomaly Detection

2 code implementations26 Jul 2023 Yuni Lai, Marcin Waniek, Liying Li, Jingwen Wu, Yulin Zhu, Tomasz P. Michalak, Talal Rahwan, Kai Zhou

In addition, we conduct transfer attack experiments in a black-box setting, which show that our feature attack significantly decreases the anomaly scores of target nodes.

Graph Anomaly Detection

HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis

no code implementations26 May 2023 Christoforos Vasilatos, Manaar Alam, Talal Rahwan, Yasir Zaki, Michail Maniatakos

As the use of Large Language Models (LLMs) in text generation tasks proliferates, concerns arise over their potential to compromise academic integrity.

Specificity Text Generation

Human intuition as a defense against attribute inference

no code implementations24 Apr 2023 Marcin Waniek, Navya Suri, Abdullah Zameek, Bedoor AlShebli, Talal Rahwan

Attribute inference - the process of analyzing publicly available data in order to uncover hidden information - has become a major threat to privacy, given the recent technological leap in machine learning.

Attribute

China and the U.S. produce more impactful AI research when collaborating together

1 code implementation21 Apr 2023 Bedoor AlShebli, Shahan Ali Memon, James A. Evans, Talal Rahwan

Given AI's massive potential, as well as the fierce geopolitical tensions between the two nations, a number of policies have been put in place that discourage AI scientists from migrating to, or collaborating with, the other country.

Attacking Similarity-Based Link Prediction in Social Networks

no code implementations22 Sep 2018 Kai Zhou, Tomasz P. Michalak, Talal Rahwan, Marcin Waniek, Yevgeniy Vorobeychik

We offer a comprehensive algorithmic investigation of the problem of attacking similarity-based link prediction through link deletion, focusing on two broad classes of such approaches, one which uses only local information about target links, and another which uses global network information.

Social and Information Networks Cryptography and Security

Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

no code implementations1 Sep 2018 Marcin Waniek, Kai Zhou, Yevgeniy Vorobeychik, Esteban Moro, Tomasz P. Michalak, Talal Rahwan

Link prediction is one of the fundamental research problems in network analysis.

Social and Information Networks Cryptography and Security 91D30 (Primary) 68T20 (Secondary) G.2.2; J.4

Game-theoretic Network Centrality: A Review

no code implementations31 Dec 2017 Mateusz K. Tarkowski, Tomasz P. Michalak, Talal Rahwan, Michael Wooldridge

Game-theoretic centrality is a flexible and sophisticated approach to identify the most important nodes in a network.

Hiding Individuals and Communities in a Social Network

no code implementations1 Aug 2016 Marcin Waniek, Tomasz Michalak, Talal Rahwan, Michael Wooldridge

With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools?

Social and Information Networks Physics and Society 91D30 (Primary) 68T20 (Secondary) G.2.2; J.4

An Anytime Algorithm for Optimal Coalition Structure Generation

no code implementations15 Jan 2014 Talal Rahwan, Sarvapali Dyanand Ramchurn, Nicholas Robert Jennings, Andrea Giovannucci

The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one.

Bounding the Estimation Error of Sampling-based Shapley Value Approximation

1 code implementation18 Jun 2013 Sasan Maleki, Long Tran-Thanh, Greg Hines, Talal Rahwan, Alex Rogers

While this algorithm provides a bound on the approximation error, this bound is \textit{asymptotic}, meaning that it only holds when the number of samples increases to infinity.

Computer Science and Game Theory

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