401 research outputs found
Online Influence Maximization in Non-Stationary Social Networks
Social networks have been popular platforms for information propagation. An
important use case is viral marketing: given a promotion budget, an advertiser
can choose some influential users as the seed set and provide them free or
discounted sample products; in this way, the advertiser hopes to increase the
popularity of the product in the users' friend circles by the world-of-mouth
effect, and thus maximizes the number of users that information of the
production can reach. There has been a body of literature studying the
influence maximization problem. Nevertheless, the existing studies mostly
investigate the problem on a one-off basis, assuming fixed known influence
probabilities among users, or the knowledge of the exact social network
topology. In practice, the social network topology and the influence
probabilities are typically unknown to the advertiser, which can be varying
over time, i.e., in cases of newly established, strengthened or weakened social
ties. In this paper, we focus on a dynamic non-stationary social network and
design a randomized algorithm, RSB, based on multi-armed bandit optimization,
to maximize influence propagation over time. The algorithm produces a sequence
of online decisions and calibrates its explore-exploit strategy utilizing
outcomes of previous decisions. It is rigorously proven to achieve an
upper-bounded regret in reward and applicable to large-scale social networks.
Practical effectiveness of the algorithm is evaluated using both synthetic and
real-world datasets, which demonstrates that our algorithm outperforms previous
stationary methods under non-stationary conditions.Comment: 10 pages. To appear in IEEE/ACM IWQoS 2016. Full versio
Defending Elections Against Malicious Spread of Misinformation
The integrity of democratic elections depends on voters' access to accurate
information. However, modern media environments, which are dominated by social
media, provide malicious actors with unprecedented ability to manipulate
elections via misinformation, such as fake news. We study a zero-sum game
between an attacker, who attempts to subvert an election by propagating a fake
new story or other misinformation over a set of advertising channels, and a
defender who attempts to limit the attacker's impact. Computing an equilibrium
in this game is challenging as even the pure strategy sets of players are
exponential. Nevertheless, we give provable polynomial-time approximation
algorithms for computing the defender's minimax optimal strategy across a range
of settings, encompassing different population structures as well as models of
the information available to each player. Experimental results confirm that our
algorithms provide near-optimal defender strategies and showcase variations in
the difficulty of defending elections depending on the resources and knowledge
available to the defender.Comment: Full version of paper accepted to AAAI 201
Constrained Signaling in Auction Design
We consider the problem of an auctioneer who faces the task of selling a good
(drawn from a known distribution) to a set of buyers, when the auctioneer does
not have the capacity to describe to the buyers the exact identity of the good
that he is selling. Instead, he must come up with a constrained signalling
scheme: a (non injective) mapping from goods to signals, that satisfies the
constraints of his setting. For example, the auctioneer may be able to
communicate only a bounded length message for each good, or he might be legally
constrained in how he can advertise the item being sold. Each candidate
signaling scheme induces an incomplete-information game among the buyers, and
the goal of the auctioneer is to choose the signaling scheme and accompanying
auction format that optimizes welfare. In this paper, we use techniques from
submodular function maximization and no-regret learning to give algorithms for
computing constrained signaling schemes for a variety of constrained signaling
problems
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