Scalable Cost-Aware Multi-Way Influence Maximization

Scalable Cost-Aware Multi-Way Influence Maximization

Hong-Han Shuai National Taiwan University
d99942020@ntu.edu.tw
   Hong-Han Shuai National Taiwan University
d99942020@ntu.edu.tw
   Hong-Han Shuai National Taiwan University
d99942020@ntu.edu.tw
   Hong-Han Shuai National Taiwan University
d99942020@ntu.edu.tw
Abstract
Viral Marketing; Social Network Analysis; Independent Cascade;

I Introduction

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 2016111http://usatoday30.usatoday.com/money/economy/story/2012-05-14/social-media-economy-companies/55029088/1. On the other hand, general advertisement channels such as TV, and newspaper are not dead yet. While there has been a significant drop since the rise of the Internet age, 116.3 million Americans have a television according to a Nielsen 2014 report.222http://www.nielsen.com/us/en/insights/news/2014/nielsen-estimates-116-3-million-tv-homes-in-the-us.html Despite of the prevalence of social media marketing, TV is still an important traditional marketing method companies should consider advertising on. Advertising efforts on TV or radio has the benefit of reaching a mass audience. A recent line of research also focuses on how to model and quantify the influence from external (e.g., TV, Online News) and internal (i.e., friends, followees) exposures together [8, 4].

Consider the following scenario as a motivating example. A telecom firm attempts to market the new plan through multiple ways, e.g., TV commercial, social media, cold calls. The company has limited budgets on each advertising way such that it can only broadcast the TV commercials several times or select some initial users in the online network to adopt it (by giving them discount or free phone accessories). The ideal case of the company is that the TV audiences love the advertisement and adopt the new plan or selected seed users love the new plan. Afterward, the initial users start canvassing their friends for the new plan on the social network, and their friends will influence their friends’ friends and so on, and thus through the viral marketing a large population in the social network would adopt the new plan.

The above marketing strategy can be regarded as a combination of traditional media marketing and social media marketing. The problems are how many the budgets should be allocated to each advertising way and whom to select as the initial users so that they eventually influence the largest number of people in the network. Moreover, since the goal is to maximize the revenue, it is desirable to construct a precise cost model. Take the telecom case as an example, when users became the member of the company, the fees may be reduced due to the discounts on all calls made within the intra-network.

With this objective in mind, we formulate a new fundamental optimization problem, named Cost-Aware Multi-Way Influence maXimization (CAMAIX). The problem is given a social graph , where each node represents a candidate person and is associated with an activation probability vector of traditional media, and each edge has a social influence probability to indicate the influence power between the two persons. Given the user-specified budget upper bound for each advertising way and the precise cost model, the goal of CAMAIX is to automatically allocate budgets and select seed users which maximizes the total revenue.

There are three major challenges of CAMAIX: i) The spread maximization problem in the Independent Cascade (IC) model [6] suffers from the expensive computation problem since the difficulty of the influence spread given a seed set is -hard. Also, instead of finding an exact algorithm, Monte-Carlo simulations of the influence cascade model run a large number of times in order to obtain a correct estimation of the spread. ii) The number of seed nodes is non-fixed, which is different from traditional influence maximization problem and complicates the problem. Let denote the number of nodes in . The enumeration approach for selecting seed nodes needs to evaluate candidate groups, whereas the enumeration approach for selecting non-fixed seed nodes is .333It is worth noting that the cost of activating seed nodes is considered so that the total revenue will be reduced when wasting budgets on users who are really not willing to use. iii) The budget allocation problem needs to deal with the interplay between different advertising ways and be extended to adopt more complicated real settings.

Aiming to efficiently solve the multi-way influence maximization with more sophisticated real settings, we systematically explore various heuristics, including Social Influence Pruning (SIP) and Adaptive Budget Allocation (ABA), to design our algorithm Intermediate Seeds Selection with Budget Allocation (ISSBA). The idea of SIP is to incrementally construct the best seed set by maintaining a number of intermediate subsets. Therefore, by iteratively expanding the best intermediate seed sets from the subsets obtained in previous iterations, SIP finds seed sets with good quality efficiently. Also, we prove the performance bound of the proposed algorithm is . On the other hand, given the upper bound of the budget for each advertising way, ABA efficiently calculates the optimal budget for each advertising way via dynamic programming.

The contribution of this paper is listed as follows.

  • We formulate a new optimization problem, namely CAMAIX, to consider the multi-way influence maximization with a sophisticated cost model, which is -hard. To the best of the our knowledge, there is no real system or existing work in the literature that efficiently addresses the issue of multi-way influence maximization based on real settings.

  • We design Algorithm ISSBA to find the solution to CAMAIX with an approximation ratio. Experimental results demonstrate that the solution returned by ISSBA it perform the baseline algorithms in both solution quality and execution time on the large-scale datasets.

The rest of this paper is organized as follows. Section II formulates the CAMAIX problem and surveys related work. Section III presents ISSBA with social influence pruning and adaptive budget allocation. We report the experimental results in Section IV and conclude this paper in Section V.

Ii Preliminary

Ii-a Problem Definition

Let denote the seed set. A user has probability to be activated as a seed, i.e., , if the advertisement is sent to through a multiple advertising ways as follows.

(1)

The activation probability of broadcasting advertisements , such as TV and billboard, is the product of the probability that the advertisement broadcasts to user and the probability of user being activated by broadcasting advertisement. If a user is activated, the propagation starts from to his neighbors with probability . Moreover, let denote the cost for each advertising way on node , where is the advertise way set. Notice that for broadcasting advertisements is the total advertisement expense divided by the number of seed nodes .

Given a directed social network , where node and each edge are associated with an initial fee and an influence probability that user activates respectively, an advertising way set , and a budget and a cost for each advertising way on node , this paper studies a new optimization problem called Cost-Aware Multi-Way Influence Maximization (CAMWIM) for finding the optimal budget and the seed set of vertices to maximize the revenue , i.e.,

where and represent the probability that the user will be activated with the seed set and the discount of activating related to its neighbor , respectively. The discount is suitable for many different scenarios, such as telecom (intra-network free) and direct sale (agent commission), and is set as for no-discount cases.

Ii-B Related Work

Influence maximization is to find a set of influential nodes, which are targeted as initial active nodes, to maximize the spread. The problem has been connected to the Independent Cascading (IC) model and the Linear Threshold (LT) Model models in [6]. D. Kempe et al. [6] show that the influence maximization problem is NP-Hard and propose a greedy algorithm for both IC and LT models, with the guarantee of the solution quality. However, the greedy algorithm needs Monte Carlo simulations to estimate the expected spread, which is time consuming. J. Leskovec et al. [7] proposes CELF to further speed up Monte Carlo simulations. Nevertheless, for large scale social networks, CELF is still not efficient enough. Several heuristic methods are proposed, such as degree discount [3], PMIA [2], and IRIE [5], to find initial active nodes very efficiently.

Iii Cost-Aware Influence Maximization

To tackle CAMAIX, a basic approach is to enumerate all possible seeds and combinations of budgets, and retrieve the one with largest revenue. However, the enumerative approach is not scalable since there are combinations for seed selection. To address the challenges, we propose a framework called Intermediate Seeds Selection with Budget Allocation (ISSBA) including Social Influence Pruning (SIP) and Adaptive Budget Allocation (ABA). SIP iteratively expands the best intermediate seed sets from the subset obtained in previous iterations. Moreover, we leverage the merit of MIA model to efficiently approximate the computation of Monte-Carlo simulation. Finally, ABA exploits dynamic-programming for allocating the budgets adaptively.

Iii-a Social Influence Pruning with Quality Guarantee

Here, we describe our proposed SIP in detail. SIP first constructs all one-item subsets , where each contains exactly one item . The corresponding join group for each are computed and the best sub-packs with the largest join groups are reserved 444The calculation process to obtain each is described in LABEL:wp later. For ease of understanding, we rename the best sub-packs as , , ….. IPO then generates 2-item sub-packs by adding every possible items into the best separately. For example, IPO expands into sub-packs by adding each item into . Note that during the generation of sub-packs, multiple sub-packs that contain the same items may be generated. IPO discards those additional duplicate sub-packs. Similarly, IPO computes the join group for each 2-item sub-packs and reserves the ones with the largest join groups for generating 3-item sub-packs 555For ease of understanding, the best sub-packs are renamed as , , ….. The process runs iteratively until the -item sub-packs are generated and the best -item sub-pack is returned. The pseudo code of IPO is showed as Algorithm LABEL:alg:poa.

Iii-B Approximate Influence Maximization

The spread maximization problem in the Independent Cascade (IC) model [6] suffers from the expensive computation problem since the difficulty of the influence spread given a seed set is -hard. To efficiently address this issue, an approximate IC model, called MIA, has been proposed [2, 1]. The social influence from a person to another person is effectively approximated by their maximum influence path (MIP), where the social influence on the path (,) is the maximum weight among all the possible paths from to . MIA creates a maximum influence in-arborescence, i.e., a directed tree, MIIA(,) including the union of every MIP to with the probability of social influence at least from a set of leaf nodes. The MIA model has been widely adopted to describe the phenomenon of social influence in the literature with the following definition on activation probability, which is basically the same as the acceptance probability if broadcasts friending invitations to all nodes in .

Definition 1

The activation probability of a node v in is =

Note that is the joint probability that is activated and successfully influences , and can never influence if it is not activated. Therefore, the activation probability of a node can be derived according to the activation probability of all its in-neighbors, i.e., the child nodes in the tree. Since is the set the leaf nodes, the activation probabilities of all nodes in can be efficiently derived in a bottom-up manner from toward .

Iii-C Computation Reduction of Budget Allocation via Dynamic Programming

Iv Experiment

Iv-a Experiment Setting

call detail records collected by a telecom operator

As [3] [4], we study telecommunications social networks extracted from a large amount of Call Detail Records (CDRs).

V Conclusion

The conclusion goes here. this is more of the conclusion

The authors would like to thank… more thanks here

References

  • [1] W. Chen, W. Lu, and N. Zhang. Time-critical influence maximization in social networks with time-delayed diffusion process. AAAI, 2012.
  • [2] W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD, pages 1029–1038, 2010.
  • [3] W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In KDD, pages 199–208, 2009.
  • [4] M. Eftekhar, Y. Ganjali, and N. Koudas. Information cascade at group scale. In KDD, 2013.
  • [5] K. Jung, W. Heo, and W. Chen. Irie: Scalable and robust influence maximization in social networks. In ICDM, pages 918 –923, 2012.
  • [6] D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD, pages 137–146, 2003.
  • [7] J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In KDD, pages 420–429, 2007.
  • [8] S. Myers, C. Zhu, and J. Leskovec. Information diffusion and external influence in networks. In KDD, 2012.
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