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UID:369@lincs.fr
DTSTART;TZID=Europe/Paris:20180514T170000
DTEND;TZID=Europe/Paris:20180514T173000
DTSTAMP:20180515T061107Z
URL:https://www.lincs.fr/events/online-influence-maximization/
SUMMARY:Online influence maximization
DESCRIPTION:We will talk about the online influence maximization problem in
 social networks under the independent cascade model. Specifically\, we aim
 to learn the set of “best influencers" in a social network online with
 unknown influence probabilities while repeatedly interacting with the
 network. We address the challenges of (i) combinatorial action space\,
 since the number of feasible influencer sets grows exponentially with the
 maximum number of influencers\, and (ii) limited feedback\, since only the
 influenced portion of the network is observed. Under a stochastic
 semi-bandit feedback\, we propose and analyze IMLinUCB\, a
 computationally\nefficient UCB-based algorithm. Our bounds on the
 cumulative regret are polynomial in all quantities of interest\, achieve
 near-optimal dependence on the number of interactions and reflect the
 topology of the network and the activation probabilities of its edges\,
 thereby giving insights on the problem complexity. To the best of our
 knowledge\, these are the first such results. Our experiments show that in
 several representative graph topologies\, the regret of IMLinUCB scales as
 suggested by our upper bounds. IMLinUCB permits linear generalization and
 thus is both statistically and computationally suitable for large-scale
 problems. Our experiments also show that IMLinUCB with linear
 generalization can lead to low regret in real-world online influence
 maximization.
CATEGORIES:Seminars,Youtube
LOCATION:LINCS / EIT Digital\, 23 avenue d'Italie\, 75013 Paris\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=23 avenue d'Italie\, 75013
 Paris\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS / EIT Digital:geo:0,0
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TZID:Europe/Paris
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DTSTART:20180325T030000
TZOFFSETFROM:+0100
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