We review classic results and recent progress on equilibrium analysis, dynamics, and
optimal interventions in network games with both continuous and discrete strategy sets. We …

We show that prominent centrality measures in network analysis are all based on additively
separable and linear treatments of statistics that capture a node's position in the network …

We study games in which a network mediates strategic spillovers and externalities among
the players. How does a planner optimally target interventions that change individuals' …

We propose a way to approximate games played over networks of increasing size by using
the graph limiting concept of graphon. To this end, we introduce the new class of graphon …

As relational datasets modeled as graphs keep increasing in size and their data-acquisition
is permeated by uncertainty, graph-based analysis techniques can become computationally …

Misinformation has emerged as a major societal challenge in the wake of the 2016 US
elections, Brexit, and the COVID-19 pandemic. One of the most active areas of inquiry into …

The tendency for individuals to form social ties with others who are similar to themselves,
known as homophily, is one of the most robust sociological principles. Since this …

In this paper, we present a unifying framework for analyzing equilibria and designing
interventions for large network games sampled from a stochastic network formation process …

We consider the problem of estimating a network's eigenvector centrality only from data on
the nodes, with no information about network topology. Leveraging the versatility of graph …

We study the spread of misinformation in a social network characterized by unequal access
to learning resources. Agents use social learning to uncover an unknown state of the world …