26,418 research outputs found
IMRank: Influence Maximization via Finding Self-Consistent Ranking
Influence maximization, fundamental for word-of-mouth marketing and viral
marketing, aims to find a set of seed nodes maximizing influence spread on
social network. Early methods mainly fall into two paradigms with certain
benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one,
give a guaranteed accuracy relying on the accurate approximation of influence
spread with high computational cost; (2)Heuristic algorithms, estimating
influence spread using efficient heuristics, have low computational cost but
unstable accuracy.
We first point out that greedy algorithms are essentially finding a
self-consistent ranking, where nodes' ranks are consistent with their
ranking-based marginal influence spread. This insight motivates us to develop
an iterative ranking framework, i.e., IMRank, to efficiently solve influence
maximization problem under independent cascade model. Starting from an initial
ranking, e.g., one obtained from efficient heuristic algorithm, IMRank finds a
self-consistent ranking by reordering nodes iteratively in terms of their
ranking-based marginal influence spread computed according to current ranking.
We also prove that IMRank definitely converges to a self-consistent ranking
starting from any initial ranking. Furthermore, within this framework, a
last-to-first allocating strategy and a generalization of this strategy are
proposed to improve the efficiency of estimating ranking-based marginal
influence spread for a given ranking. In this way, IMRank achieves both
remarkable efficiency and high accuracy by leveraging simultaneously the
benefits of greedy algorithms and heuristic algorithms. As demonstrated by
extensive experiments on large scale real-world social networks, IMRank always
achieves high accuracy comparable to greedy algorithms, with computational cost
reduced dramatically, even about times faster than other scalable
heuristics.Comment: 10 pages, 8 figures, this paper has been submitted to SIGIR201
Fast Budgeted Influence Maximization over Multi-Action Event Logs
In a social network, influence maximization is the problem of identifying a
set of users that own the maximum {\it influence ability} across the network.
In this paper, a novel credit distribution (CD) based model, termed as the
multi-action CD (mCD) model, is introduced to quantify the influence ability of
each user, which works with practical datasets where one type of action could
be recorded for multiple times. Based on this model, influence maximization is
formulated as a submodular maximization problem under a general knapsack
constraint, which is NP-hard. An efficient streaming algorithm with one-round
scan over the user set is developed to find a suboptimal solution.
Specifically, we first solve a special case of knapsack constraints, i.e., a
cardinality constraint, and show that the developed streaming algorithm can
achieve ()-approximation of the optimality. Furthermore,
for the general knapsack case, we show that the modified streaming algorithm
can achieve ()-approximation of the optimality. Finally,
experiments are conducted over real Twitter dataset and demonstrate that the
mCD model enjoys high accuracy compared to the conventional CD model in
estimating the total number of people who get influenced in a social network.
Moreover, through the comparison to the conventional CD, non-CD models, and the
mCD model with the greedy algorithm on the performance of the influence
maximization problem, we show the effectiveness and efficiency of the proposed
mCD model with the streaming algorithm
On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks
Identifying the most influential individuals can provide invaluable help in
developing and deploying effective viral marketing strategies. Previous studies
mainly focus on designing efficient algorithms or heuristics to find top-K
influential nodes on a given static social network. While, as a matter of fact,
real-world social networks keep evolving over time and a recalculation upon the
changed network inevitably leads to a long running time, significantly
affecting the efficiency. In this paper, we observe from real-world traces that
the evolution of social network follows the preferential attachment rule and
the influential nodes are mainly selected from high-degree nodes. Such
observations shed light on the design of IncInf, an incremental approach that
can efficiently locate the top-K influential individuals in evolving social
networks based on previous information instead of calculation from scratch. In
particular, IncInf quantitatively analyzes the influence spread changes of
nodes by localizing the impact of topology evolution to only local regions, and
a pruning strategy is further proposed to effectively narrow the search space
into nodes experiencing major increases or with high degrees. We carried out
extensive experiments on real-world dynamic social networks including Facebook,
NetHEPT, and Flickr. Experimental results demonstrate that, compared with the
state-of-the-art static heuristic, IncInf achieves as much as 21X speedup in
execution time while maintaining matching performance in terms of influence
spread
Measuring and Maximizing Influence via Random Walk in Social Activity Networks
With the popularity of OSNs, finding a set of most influential users (or
nodes) so as to trigger the largest influence cascade is of significance. For
example, companies may take advantage of the "word-of-mouth" effect to trigger
a large cascade of purchases by offering free samples/discounts to those most
influential users. This task is usually modeled as an influence maximization
problem, and it has been widely studied in the past decade. However,
considering that users in OSNs may participate in various kinds of online
activities, e.g., giving ratings to products, joining discussion groups, etc.,
influence diffusion through online activities becomes even more significant.
In this paper, we study the impact of online activities by formulating the
influence maximization problem for social-activity networks (SANs) containing
both users and online activities. To address the computation challenge, we
define an influence centrality via random walks to measure influence, then use
the Monte Carlo framework to efficiently estimate the centrality in SANs.
Furthermore, we develop a greedy-based algorithm with two novel optimization
techniques to find the most influential users. By conducting extensive
experiments with real-world datasets, we show our approach is more efficient
than the state-of-the-art algorithm IMM[17] when we needs to handle large
amount of online activities.Comment: 19 page
Scalable Cost-Aware Multi-Way Influence Maximization
Viral marketing is different from other marketing strategies since it
leverages the influence power in intimate relationship, e.g., close friends,
family members, couples. Due to the development and popularity of social
networking services, such as Facebook, Twitter, and Pinterest, the new notion
of "social media marketing" has appeared in recent years and presents new
opportunities for enabling large-scale and prevalent viral marketing online. To
boost the growth of their sales, business is embracing social media in a big
way. According to USA Today, the sales of software to run corporate social
networks will grow 61\% a year and be a billion business by 2016
A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop
Recently, there is a surge of interest in using point processes to model
continuous-time user activities. This framework has resulted in novel models
and improved performance in diverse applications. However, most previous works
focus on the "open loop" setting where learned models are used for predictive
tasks. Typically, we are interested in the "closed loop" setting where a policy
needs to be learned to incorporate user feedbacks and guide user activities to
desirable states. Although point processes have good predictive performance, it
is not clear how to use them for the challenging closed loop activity guiding
task. In this paper, we propose a framework to reformulate point processes into
stochastic differential equations, which allows us to extend methods from
stochastic optimal control to address the activity guiding problem. We also
design an efficient algorithm, and show that our method guides user activities
to desired states more effectively than the state of the art
Learning and Optimization with Submodular Functions
In many naturally occurring optimization problems one needs to ensure that
the definition of the optimization problem lends itself to solutions that are
tractable to compute. In cases where exact solutions cannot be computed
tractably, it is beneficial to have strong guarantees on the tractable
approximate solutions. In order operate under these criterion most optimization
problems are cast under the umbrella of convexity or submodularity. In this
report we will study design and optimization over a common class of functions
called submodular functions. Set functions, and specifically submodular set
functions, characterize a wide variety of naturally occurring optimization
problems, and the property of submodularity of set functions has deep
theoretical consequences with wide ranging applications. Informally, the
property of submodularity of set functions concerns the intuitive "principle of
diminishing returns. This property states that adding an element to a smaller
set has more value than adding it to a larger set. Common examples of
submodular monotone functions are entropies, concave functions of cardinality,
and matroid rank functions; non-monotone examples include graph cuts, network
flows, and mutual information.
In this paper we will review the formal definition of submodularity; the
optimization of submodular functions, both maximization and minimization; and
finally discuss some applications in relation to learning and reasoning using
submodular functions.Comment: Tech Report - USC Computer Science CS-599, Convex and Combinatorial
Optimizatio
Seed Selection and Social Coupon Allocation for Redemption Maximization in Online Social Networks
Online social networks have become the medium for efficient viral marketing
exploiting social influence in information diffusion. However, the emerging
application Social Coupon (SC) incorporating social referral into coupons
cannot be efficiently solved by previous researches which do not take into
account the effect of SC allocation. The number of allocated SCs restricts the
number of influenced friends for each user. In the paper, we investigate not
only the seed selection problem but also the effect of SC allocation for
optimizing the redemption rate which represents the efficiency of SC
allocation. Accordingly, we formulate a problem named Seed Selection and SC
allocation for Redemption Maximization (S3CRM) and prove the hardness of S3CRM.
We design an effective algorithm with a performance guarantee, called Seed
Selection and Social Coupon allocation algorithm. For S3CRM, we introduce the
notion of marginal redemption to evaluate the efficiency of investment in seeds
and SCs. Moreover, for a balanced investment, we develop a new graph structure
called guaranteed path, to explore the opportunity to optimize the redemption
rate. Finally, we perform a comprehensive evaluation on our proposed algorithm
with various baselines. The results validate our ideas and show the
effectiveness of the proposed algorithm over baselines.Comment: Full version (accepted by ICDE 2019
An Approximate Marginal Spread Computation Approach for the Budgeted Influence Maximization with Delay
In this paper, we study the Budgeted Influence Maximization with Delay
Problem, for which the number of literature are limited. We propose an
approximate marginal spread computation\mbox{-}based approach for solving this
problem. The proposed methodology has been implemented with three benchmark
social network datasets and the obtained results are compared with the existing
methods from the literature. Experimental results show that the proposed
approach is able to select seed nodes which leads to more number of influential
nodes with reasonable computational time
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
Influence maximization, defined as a problem of finding a set of seed nodes
to trigger a maximized spread of influence, is crucial to viral marketing on
social networks. For practical viral marketing on large scale social networks,
it is required that influence maximization algorithms should have both
guaranteed accuracy and high scalability. However, existing algorithms suffer a
scalability-accuracy dilemma: conventional greedy algorithms guarantee the
accuracy with expensive computation, while the scalable heuristic algorithms
suffer from unstable accuracy.
In this paper, we focus on solving this scalability-accuracy dilemma. We
point out that the essential reason of the dilemma is the surprising fact that
the submodularity, a key requirement of the objective function for a greedy
algorithm to approximate the optimum, is not guaranteed in all conventional
greedy algorithms in the literature of influence maximization. Therefore a
greedy algorithm has to afford a huge number of Monte Carlo simulations to
reduce the pain caused by unguaranteed submodularity. Motivated by this
critical finding, we propose a static greedy algorithm, named StaticGreedy, to
strictly guarantee the submodularity of influence spread function during the
seed selection process. The proposed algorithm makes the computational expense
dramatically reduced by two orders of magnitude without loss of accuracy.
Moreover, we propose a dynamical update strategy which can speed up the
StaticGreedy algorithm by 2-7 times on large scale social networks.Comment: 10 pages, 8 figures, this paper has been published in the proceedings
of CIKM201
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