Alessandro Panconesi - Univ. La Sapienza, Rome

Alessandro Panconesi
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Contact Details

Name
Alessandro Panconesi
Affiliation
Univ. La Sapienza, Rome
City
Roma
Country
Italy

Pubs By Year

Pub Categories

 
Computer Science - Distributed; Parallel; and Cluster Computing (2)
 
Computer Science - Computational Complexity (2)
 
Mathematics - Probability (1)
 
Computer Science - Data Structures and Algorithms (1)
 
Physics - Physics and Society (1)
 
Computer Science - Discrete Mathematics (1)

Publications Authored By Alessandro Panconesi

In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users. We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form. Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others. Read More

Recently, due to the widespread diffusion of smart-phones, mobile puzzle games have experienced a huge increase in their popularity. A successful puzzle has to be both captivating and challenging, and it has been suggested that this features are somehow related to their computational complexity \cite{Eppstein}. Indeed, many puzzle games --such as Mah-Jongg, Sokoban, Candy Crush, and 2048, to name a few-- are known to be NP-hard \cite{CondonFLS97, culberson1999sokoban, GualaLN14, Mehta14a}. Read More

The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network, which often require an unrealistically large number of resources (e.g. Read More

Randomized gossip is one of the most popular way of disseminating information in large scale networks. This method is appreciated for its simplicity, robustness, and efficiency. In the "push" protocol, every informed node selects, at every time step (a. Read More

We show that Naming-- the existence of distinct IDs known to all-- is a hidden but necessary assumption of Herlihy's universality result for Consensus. We then show in a very precise sense that Naming is harder than Consensus and bring to the surface some important differences existing between popular shared memory models. Read More