53,271 research outputs found
Towards Profit Maximization for Online Social Network Providers
Online Social Networks (OSNs) attract billions of users to share information
and communicate where viral marketing has emerged as a new way to promote the
sales of products. An OSN provider is often hired by an advertiser to conduct
viral marketing campaigns. The OSN provider generates revenue from the
commission paid by the advertiser which is determined by the spread of its
product information. Meanwhile, to propagate influence, the activities
performed by users such as viewing video ads normally induce diffusion cost to
the OSN provider. In this paper, we aim to find a seed set to optimize a new
profit metric that combines the benefit of influence spread with the cost of
influence propagation for the OSN provider. Under many diffusion models, our
profit metric is the difference between two submodular functions which is
challenging to optimize as it is neither submodular nor monotone. We design a
general two-phase framework to select seeds for profit maximization and develop
several bounds to measure the quality of the seed set constructed. Experimental
results with real OSN datasets show that our approach can achieve high
approximation guarantees and significantly outperform the baseline algorithms,
including state-of-the-art influence maximization algorithms.Comment: INFOCOM 2018 (Full version), 12 page
LiveRank: How to Refresh Old Datasets
This paper considers the problem of refreshing a dataset. More precisely ,
given a collection of nodes gathered at some time (Web pages, users from an
online social network) along with some structure (hyperlinks, social
relationships), we want to identify a significant fraction of the nodes that
still exist at present time. The liveness of an old node can be tested through
an online query at present time. We call LiveRank a ranking of the old pages so
that active nodes are more likely to appear first. The quality of a LiveRank is
measured by the number of queries necessary to identify a given fraction of the
active nodes when using the LiveRank order. We study different scenarios from a
static setting where the Liv-eRank is computed before any query is made, to
dynamic settings where the LiveRank can be updated as queries are processed.
Our results show that building on the PageRank can lead to efficient LiveRanks,
for Web graphs as well as for online social networks
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