67 research outputs found
Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach
Most people simultaneously belong to several distinct social networks, in
which their relations can be different. They have opinions about certain
topics, which they share and spread on these networks, and are influenced by
the opinions of other persons. In this paper, we build upon this observation to
propose a new nodal centrality measure for multiplex networks. Our measure,
called Opinion centrality, is based on a stochastic model representing opinion
propagation dynamics in such a network. We formulate an optimization problem
consisting in maximizing the opinion of the whole network when controlling an
external influence able to affect each node individually. We find a
mathematical closed form of this problem, and use its solution to derive our
centrality measure. According to the opinion centrality, the more a node is
worth investing external influence, and the more it is central. We perform an
empirical study of the proposed centrality over a toy network, as well as a
collection of real-world networks. Our measure is generally negatively
correlated with existing multiplex centrality measures, and highlights
different types of nodes, accordingly to its definition
Differential Games of Competition in Online Content Diffusion
Access to online contents represents a large share of the Internet traffic.
Most such contents are multimedia items which are user-generated, i.e., posted
online by the contents' owners. In this paper we focus on how those who provide
contents can leverage online platforms in order to profit from their large base
of potential viewers.
Actually, platforms like Vimeo or YouTube provide tools to accelerate the
dissemination of contents, i.e., recommendation lists and other re-ranking
mechanisms. Hence, the popularity of a content can be increased by paying a
cost for advertisement: doing so, it will appear with some priority in the
recommendation lists and will be accessed more frequently by the platform
users.
Ultimately, such acceleration mechanism engenders a competition among online
contents to gain popularity. In this context, our focus is on the structure of
the acceleration strategies which a content provider should use in order to
optimally promote a content given a certain daily budget. Such a best response
indeed depends on the strategies adopted by competing content providers. Also,
it is a function of the potential popularity of a content and the fee paid for
the platform advertisement service.
We formulate the problem as a differential game and we solve it for the
infinite horizon case by deriving the structure of certain Nash equilibria of
the game
Online Multi-Agent Decentralized Byzantine-robust Gradient Estimation
In this paper, we propose an iterative scheme for distributed
Byzantineresilient estimation of a gradient associated with a black-box model.
Our algorithm is based on simultaneous perturbation, secure state estimation
and two-timescale stochastic approximations. We also show the performance of
our algorithm through numerical experiments
Online Learning with Adversaries: A Differential-Inclusion Analysis
We introduce an observation-matrix-based framework for fully asynchronous
online Federated Learning (FL) with adversaries. In this work, we demonstrate
its effectiveness in estimating the mean of a random vector. Our main result is
that the proposed algorithm almost surely converges to the desired mean
This makes ours the first asynchronous FL method to have an a.s. convergence
guarantee in the presence of adversaries. We derive this convergence using a
novel differential-inclusion-based two-timescale analysis. Two other highlights
of our proof include (a) the use of a novel Lyapunov function to show that
is the unique global attractor for our algorithm's limiting dynamics, and
(b) the use of martingale and stopping-time theory to show that our algorithm's
iterates are almost surely bounded.Comment: 6 pages, 2 figure
Posting behavior in Social Networks and Content Active Filtering
International audience—In this paper, we have two objectives: First we model the posting behavior in Social Networks in topics which have negative externalities, and the second objective is to propose content active filtering in order to increase content diversity. By negative externalities, we mean that when the quantity of posted contents about some topic increases the popularity of posted contents decreases. We introduce a dynamical model to describe the posting behavior of users taking into account these externalities. Our model is based on stochastic approximations and sufficient conditions are provided to ensure its convergence to a unique rest point. We provide a close form of this rest point. Content Active Filtering (CAF) are actions taken by the administrator of the Social Network in order to promote some objectives related to the quantity of contents posted in various topics. As objective of the CAF we shall consider maximizing the diversity of posted contents
Controlling the Katz-Bonacich Centrality in Social Network: Application to gossip in Online Social Networks
International audienceRecent papers studied the control of spectral centrality measures of a network by manipulating the topology of the network. We extend these works by focusing on a specific spectral centrality measure, the Katz-Bonacich centrality. The optimization of the Katz-Bonacich centrality using a topological control is called the Katz-Bonacich optimization problem. We first prove that this problem is equivalent to a linear optimization problem. Thus, in the context of large graphs, we can use state of the art algorithms. We provide a specific applications of the Katz-Bonacich centrality minimization problem based on the minimization of gossip propagation and make some experiments on real networks
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