arXiv subjects

Yigit E. Bayiz

Publications and source records attributed to Yigit E. Bayiz.

3 recordsLinked to original sources

arXiv2024

Relationship Design for Socially-Aware Behavior in Static Games

Autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. We present a new approach to model socially-aware decision-making that includes two key elements: bounded rationality and inter-agent relationships. We capture the interagent relationships by introducing a novel model called a relationship game and encode agents' bounded rationality using quantal response equilibria. For each relationship game, we define a social cost function and formulate a mechanism design problem to optimize weights for relationships that minimize social cost at the equilibrium. We address the multiplicity of equilibria by presenting the problem in two forms: Min-Max and Min-Min, aimed respectively at minimization of the highest and lowest social costs in the equilibria. We compute the quantal response equilibrium by solving a least-squares problem defined with its Karush-Kuhn-Tucker conditions, and propose two projected gradient descent algorithms to solve the mechanism design problems. Numerical results, including two-lane congestion and congestion with an ambulance, confirm that these algorithms consistently reach the equilibrium with the intended social costs.

cs.MA↗

arXiv2022

Countering Misinformation on Social Networks Using Graph Alterations

We restrict the propagation of misinformation in a social-media-like environment while preserving the spread of correct information. We model the environment as a random network of users in which each news item propagates in the network in consecutive cascades. Existing studies suggest that the cascade behaviors of misinformation and correct information are affected differently by user polarization and reflexivity. We show that this difference can be used to alter network dynamics in a way that selectively hinders the spread of misinformation content. To implement these alterations, we introduce an optimization-based probabilistic dropout method that randomly removes connections between users to achieve minimal propagation of misinformation. We use disciplined convex programming to optimize these removal probabilities over a reduced space of possible network alterations. We test the algorithm's effectiveness using simulated social networks. In our tests, we use both synthetic network structures based on stochastic block models, and natural network structures that are generated using random sampling of a dataset collected from Twitter. The results show that on average the algorithm decreases the cascade size of misinformation content by up to $70\%$ in synthetic network tests and up to $45\%$ in natural network tests while maintaining a branching ratio of at least $1.5$ for correct information.

cs.SI↗

arXiv2020

Graph Signal Processing: Vertex Multiplication

On the Euclidean domains of classical signal processing, linking of signal samples to the underlying coordinate structure is straightforward. While graph adjacency matrices totally define the quantitative associations among the underlying graph vertices, a major problem in graph signal processing is the lack of explicit association of vertices with an underlying quantitative coordinate structure. To make this link, we propose an operation, called the vertex multiplication, which is defined for graphs and can operate on graph signals. Vertex multiplication, which generalizes the coordinate multiplication operation in time series signals, can be interpreted as an operator which assigns a coordinate structure to a graph. By using the graph domain extension of differentiation and graph Fourier transform (GFT), vertex multiplication is defined such that it shows Fourier duality, which states that differentiation and coordinate multiplication operations are duals of each other under Fourier transformation (FT). The proposed definition is shown to reduce to coordinate multiplication for graphs corresponding to time series. Numerical examples are also presented.

eess.SP↗