959 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
Importance Sketching of Influence Dynamics in Billion-scale Networks
The blooming availability of traces for social, biological, and communication
networks opens up unprecedented opportunities in analyzing diffusion processes
in networks. However, the sheer sizes of the nowadays networks raise serious
challenges in computational efficiency and scalability.
In this paper, we propose a new hyper-graph sketching framework for inflence
dynamics in networks. The central of our sketching framework, called SKIS, is
an efficient importance sampling algorithm that returns only non-singular
reverse cascades in the network. Comparing to previously developed sketches
like RIS and SKIM, our sketch significantly enhances estimation quality while
substantially reducing processing time and memory-footprint. Further, we
present general strategies of using SKIS to enhance existing algorithms for
influence estimation and influence maximization which are motivated by
practical applications like viral marketing. Using SKIS, we design high-quality
influence oracle for seed sets with average estimation error up to 10x times
smaller than those using RIS and 6x times smaller than SKIM. In addition, our
influence maximization using SKIS substantially improves the quality of
solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x
memory reduction for the fastest RIS-based DSSA algorithm, while maintaining
the same theoretical guarantees.Comment: 12 pages, to appear in ICDM 2017 as a regular pape
Big Networks: Analysis and Optimal Control
The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world `Big Data\u27 requirement.
This research aims at affirmatively boosting the modern algorithmic efficiency to approach practical requirements. That is developing a ground-breaking class of algorithms that provide simultaneously both provably good solution qualities and low time and space complexities. Specifically, I target the important yet challenging problems in the three main areas:
Information Diffusion: Analyzing and maximizing the influence in networks and extending results for different variations of the problems.
Community Detection: Finding communities from multiple sources of information.
Security and Privacy: Assessing organization vulnerability under targeted-cyber attacks via social networks
Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks
Online social network has been one of the most important platforms for viral
marketing. Most of existing researches about diffusion of adoptions of new
products on networks are about one diffusion. That is, only one piece of
information about the product is spread on the network. However, in fact, one
product may have multiple features and the information about different features
may spread independently in social network. When a user would like to purchase
the product, he would consider all of the features of the product
comprehensively not just consider one. Based on this, we propose a novel
problem, multi-feature budgeted profit maximization (MBPM) problem, which first
considers budgeted profit maximization under multiple features propagation of
one product.
Given a social network with each node having an activation cost and a profit,
MBPM problem seeks for a seed set with expected cost no more than the budget to
make the total expected profit as large as possible. We consider MBPM problem
under the adaptive setting, where seeds are chosen iteratively and next seed is
selected according to current diffusion results. We study adaptive MBPM problem
under two models, oracle model and noise model. The oracle model assumes
conditional expected marginal profit of any node could be obtained in O(1) time
and a (1-1/e) expected approximation policy is proposed. Under the noise model,
we estimate conditional expected marginal profit of a node by modifying the
EPIC algorithm and propose an efficient policy, which could return a
(1-exp({\epsilon}-1)) expected approximation ratio. Several experiments are
conducted on six realistic datasets to compare our proposed policies with their
corresponding non-adaptive algorithms and some heuristic adaptive policies.
Experimental results show efficiencies and superiorities of our policies.Comment: 12 pages, 6 figure
- …