Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Papachristou, Marios"'
Autor:
Papachristou, Marios, Yuan, Yuan
Social networks shape opinions, behaviors, and information dissemination in human societies. As large language models (LLMs) increasingly integrate into social and professional environments, understanding their behavior within the context of social i
Externí odkaz:
http://arxiv.org/abs/2402.10659
How can individuals exchange information to learn from each other despite their privacy needs and security concerns? For example, consider individuals deliberating a contentious topic and being concerned about divulging their private experiences. Pre
Externí odkaz:
http://arxiv.org/abs/2402.08156
In various work contexts, such as meeting scheduling, collaborating, and project planning, collective decision-making is essential but often challenging due to diverse individual preferences, varying work focuses, and power dynamics among members. To
Externí odkaz:
http://arxiv.org/abs/2311.04928
Publikováno v:
IISE Transactions, 2024
We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The agents can collectively estim
Externí odkaz:
http://arxiv.org/abs/2306.15865
We investigate the structural factors that drive cascading failures in production networks, focusing on quantifying these risks with a topological resilience metric corresponding to the largest exogenous systemic shock that the production network can
Externí odkaz:
http://arxiv.org/abs/2303.12660
In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well on homophilous graphs and are leveraged by GCNs and GATs. Such archit
Externí odkaz:
http://arxiv.org/abs/2211.00550
Autor:
Papachristou, Marios, Kleinberg, Jon
We introduce a random hypergraph model for core-periphery structure. By leveraging our model's sufficient statistics, we develop a novel statistical inference algorithm that is able to scale to large hypergraphs with runtime that is practically linea
Externí odkaz:
http://arxiv.org/abs/2206.00783
Publikováno v:
Proceedings of the ACM Web Conference 2023
We study the problem of designing dynamic intervention policies for minimizing networked defaults in financial networks. Formally, we consider a dynamic version of the celebrated Eisenberg-Noe model of financial network liabilities and use this to st
Externí odkaz:
http://arxiv.org/abs/2205.13394
We exploit the core-periphery structure and the strong homophilic properties of online social networks to develop faster and more accurate algorithms for user interest prediction. The core of modern social networks consists of relatively few influent
Externí odkaz:
http://arxiv.org/abs/2107.03449
Autor:
Papachristou, Marios, Kleinberg, Jon
We study the problem of allocating bailouts (stimulus, subsidy allocations) to people participating in a financial network subject to income shocks. We build on the financial clearing framework of Eisenberg and Noe that allows the incorporation of a
Externí odkaz:
http://arxiv.org/abs/2106.07560