28,613 research outputs found
Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
The steady growth of graph data from social networks has resulted in
wide-spread research in finding solutions to the influence maximization
problem. In this paper, we propose a holistic solution to the influence
maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI)
model that closely mirrors the real-world scenarios. Under the OI model, we
introduce a novel problem of Maximizing the Effective Opinion (MEO) of
influenced users. We prove that the MEO problem is NP-hard and cannot be
approximated within a constant ratio unless P=NP. (2) We propose a heuristic
algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM
heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a
scalable algorithm capable of running within practical compute times on
commodity hardware. In addition to serving as a fundamental building block for
OSIM, EaSyIM is capable of addressing the scalability aspect - memory
consumption and running time, of the IM problem as well.
Empirically, our algorithms are capable of maintaining the deviation in the
spread always within 5% of the best known methods in the literature. In
addition, our experiments show that both OSIM and EaSyIM are effective,
efficient, scalable and significantly enhance the ability to analyze real
datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure
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
Influence Maximization with Bandits
We consider the problem of \emph{influence maximization}, the problem of
maximizing the number of people that become aware of a product by finding the
`best' set of `seed' users to expose the product to. Most prior work on this
topic assumes that we know the probability of each user influencing each other
user, or we have data that lets us estimate these influences. However, this
information is typically not initially available or is difficult to obtain. To
avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm
that estimates the influence probabilities as we sequentially try different
seed sets. We establish bounds on the performance of this procedure under the
existing edge-level feedback as well as a novel and more realistic node-level
feedback. Beyond our theoretical results, we describe a practical
implementation and experimentally demonstrate its efficiency and effectiveness
on four real datasets.Comment: 12 page
Maximizing Activity in Ising Networks via the TAP Approximation
A wide array of complex biological, social, and physical systems have
recently been shown to be quantitatively described by Ising models, which lie
at the intersection of statistical physics and machine learning. Here, we study
the fundamental question of how to optimize the state of a networked Ising
system given a budget of external influence. In the continuous setting where
one can tune the influence applied to each node, we propose a series of
approximate gradient ascent algorithms based on the Plefka expansion, which
generalizes the na\"{i}ve mean field and TAP approximations. In the discrete
setting where one chooses a small set of influential nodes, the problem is
equivalent to the famous influence maximization problem in social networks with
an additional stochastic noise term. In this case, we provide sufficient
conditions for when the objective is submodular, allowing a greedy algorithm to
achieve an approximation ratio of . Additionally, we compare the
Ising-based algorithms with traditional influence maximization algorithms,
demonstrating the practical importance of accurately modeling stochastic
fluctuations in the system
Maximizing the Diversity of Exposure in a Social Network
Social-media platforms have created new ways for citizens to stay informed
and participate in public debates. However, to enable a healthy environment for
information sharing, social deliberation, and opinion formation, citizens need
to be exposed to sufficiently diverse viewpoints that challenge their
assumptions, instead of being trapped inside filter bubbles. In this paper, we
take a step in this direction and propose a novel approach to maximize the
diversity of exposure in a social network. We formulate the problem in the
context of information propagation, as a task of recommending a small number of
news articles to selected users. We propose a realistic setting where we take
into account content and user leanings, and the probability of further sharing
an article. This setting allows us to capture the balance between maximizing
the spread of information and ensuring the exposure of users to diverse
viewpoints.
The resulting problem can be cast as maximizing a monotone and submodular
function subject to a matroid constraint on the allocation of articles to
users. It is a challenging generalization of the influence maximization
problem. Yet, we are able to devise scalable approximation algorithms by
introducing a novel extension to the notion of random reverse-reachable sets.
We experimentally demonstrate the efficiency and scalability of our algorithm
on several real-world datasets
Stability of Influence Maximization
The present article serves as an erratum to our paper of the same title,
which was presented and published in the KDD 2014 conference. In that article,
we claimed falsely that the objective function defined in Section 1.4 is
non-monotone submodular. We are deeply indebted to Debmalya Mandal, Jean
Pouget-Abadie and Yaron Singer for bringing to our attention a counter-example
to that claim.
Subsequent to becoming aware of the counter-example, we have shown that the
objective function is in fact NP-hard to approximate to within a factor of
for any .
In an attempt to fix the record, the present article combines the problem
motivation, models, and experimental results sections from the original
incorrect article with the new hardness result. We would like readers to only
cite and use this version (which will remain an unpublished note) instead of
the incorrect conference version.Comment: Erratum of Paper "Stability of Influence Maximization" which was
presented and published in the KDD1
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