Viral Marketing In Social Networks Research Articles

COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems

Combinatorial Optimization (CO) problems over graphs appear routinely in many applications such as in optimizing traffic, viral marketing in social networks, and matching for job allocation. Due to their combinatorial nature, these problems are often NP-hard. Existing approximation algorithms and heuristics rely on the search space to find the solutions and become time-consuming when this space is large. In this paper, we design a neural method called COMBHelper to reduce this space and thus improve the efficiency of the traditional CO algorithms based on node selection. Specifically, it employs a Graph Neural Network (GNN) to identify promising nodes for the solution set. This pruned search space is then fed to the traditional CO algorithms. COMBHelper also uses a Knowledge Distillation (KD) module and a problem-specific boosting module to bring further efficiency and efficacy. Our extensive experiments show that the traditional CO algorithms with COMBHelper are at least 2 times faster than their original versions.

Exploiting community and structural hole spanner for influence maximization in social networks

AbstractViral marketing is a frequently used social marketing strategy which aims to promote products on various social media. In order to devise an efficient viral marketing strategy, influence maximization problem is widely studied in social networks. Influence maximization problem tries to find a set of influential users who could influence social users maximally. However, existed influence maximization algorithms could not satisfy the needs of social marketers well. In social networks, community structure is an important feature where social users are closely connected together in groups, and structural hole spanners are those users who bridge different communities. In order to balance the effectiveness and efficiency of viral marketing in social networks, in this paper, we present to exploit the community structure and structural hole spanners in social networks for solving the influence maximization problem. Different to traditional algorithms, we devise a strategy to approximate social influences of social users to find seeds efficiently. In particular, for an internal user inside a community in social networks, we utilize his social influence to members inside this community to approximate his social influence in the whole social network. For a structural hole spanner who bridges multiple communities, we utilize his social influence to members in these communities to approximate his social influence in the whole network. Based on the approximate social influence, information diffusion model and greedy framework, we propose a Community and Structural Hole Spanner based Greedy (CSHS‐G) algorithm which devises several influence lists to store social influences of users and fast computation scheme to find seeds. Besides, to further improve the efficiency, we propose a Community and Structural Hole Spanner based Heuristic (CSHS‐H) algorithm which approximates social influences of structural hole spanners by utilizing their 2‐hops influences. We conduct a comprehensive performance evaluation on the crawled real‐world data set. Experimental results show that, compared to all of the baseline algorithms, our proposed algorithms not only have high efficiency, but also could guarantee larger influence spread.

Multiple Benefit Thresholds Problem in Online Social Networks: An Algorithmic Approach

An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit different in several realistic scenarios due to market dependence or budget constraints. As a consequence, picking a seed set for a specific threshold is not enough to come up with an effective solution. To address the disadvantages of previous works with a new approach, we study the Multiple Benefit Thresholds (MBT), a generalized version of the IT problem, as a result of this phenomenon. Given a social network that is subjected to information distribution and a set of thresholds, T={T1,T2,…,Tk},Ti>0, the issue aims to seek the seed sets S1,S2,…,Sk with the lowest possible cost so that the benefit achieved from the influence process is at the very least T1,T2,…,Tk, respectively. The main challenges of this problem are a #NP-hard problem and the estimation of the objective function #P-Hard under traditional information propagation models. In addition, adapting the exist algorithms many times to different thresholds can lead to large computational costs. To address the abovementioned challenges, we introduced Efficient Sampling for Selecting Multiple Seed Sets, an efficient technique with theoretical guarantees (ESSM). At the core of our algorithm, we developed a novel algorithmic framework that (1) can use the solution to a smaller threshold to find that of larger ones and (2) can leverage existing samples with the current solution to find that of larger ones. The extensive experiments on several real social networks were conducted in order to show the effectiveness and performance of our algorithm compared with current ones. The results indicated that our algorithm outperformed other state-of-the-art ones in terms of both the total cost and running time.

A multi-objective model for identifying valuable nodes in complex networks with minimum cost

Nowadays, social networks sites (SNs) are widely used in a variety of applications such as viral marketing. Given a huge number of users on SNs, the process of selecting appropriate users as the target set is key decision for enterprises to conduct cost-effective targeted marketing and reputation management. Several methods have been presented to find influential users for viral marketing in social networks. One of the weaknesses of previous methods is the selection of target sets who activate similar users and have high spreading ability in the whole network. In this paper, an optimization model is proposed to identify the most influential users of social networks in another view by splitting a big network into small parts as communities and considering both positive and negative interactions between users to create the influence graph. Our model takes the usefulness and similarity of the users into account and tries to select the most profitable users with the lowest possible similarity in which the number of target users is automatically determined. Assessments on real and synthetic networks indicate that the proposed method is capable to select a target set with high profit and gives better performance than other methods.

Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

Threshold Estimation Models for Linear Threshold-Based Influential User Mining in Social Networks

Influence Maximization (IM) is a popular social network mining mechanism that mines influential users for viral marketing in social networks. Most of the Influence Maximization techniques employ either the independent cascade (IC) or linear threshold (LT) model in the node activation process. In the IC model, all the active in-neighbors are given a single chance to activate a node with a particular probability whereas, in the LT model, a node is activated if the aggregated influence of all the activated in-neighbors is no less than a threshold value. Thus, the threshold plays a significant role in the LT-based influence maximization. In this paper, we comprehensively survey the different threshold values used in various IM models. Based on the survey, we observe that the current studies lack threshold estimation models. Therefore, we develop a system model and propose four threshold estimation models based on influence-weight and degree distribution. The empirical results show that our algorithms generate threshold values that resemble the thresholds used by most IM algorithms along with faster running time. Besides, the proposed models are scalable and applicable to any influence-weight estimation technique and offer narrower threshold ranges rather than the broad ranges used in many existing works.

Analysis of the Impact of Viral Marketing in Social Networks on the Purchase Intention of Consumers: A Case Study of Telegram Social Network

هدف این پژوهش، بررسی تأثیر بازاریابی ویروسی در شبکه اجتماعی تلگرام بر قصد خرید مصرف­کنندگان می­باشد. این پژوهش از نظر ماهیت، کاربردی و از نظر روش توصیفی- پیمایشی می­باشد. جامعه آماری این تحقیق، کاربران شبکه­ اجتماعی تلگرام می­باشد. برای جمع­آوری اطلاعات تحقیق از پرسشنامه استفاده شد که درمجموع نتایج این پژوهش از تجزیه‌وتحلیل 196 پرسشنامه تکمیل‌شده معتبر می‌باشد. جهت ارزیابی روایی صوری و محتوا، پرسشنامه توسط متخصصین و اساتید محترم مرور گردیده است. ارزیابی پایایی توسط آزمون آلفای کرونباخ صورت گرفته که مقدار آن 926/0 بوده است و جهت ارزیابی روایی همگرا و واگرا از طریق نرم­افزار Smart PLS (تعیین بارهای عاملی) و نرم­افزار SPSS (تعیین همبستگی 11 متغیر با یکدیگر) و جهت آزمون فرضیات تحقیق، تحلیل مسیر از طریق نرم­افزار Smart PLS انجام شده است. میزان T کسب‌شده جهت تأثیر یا رد فرضیات ملاک قرار داده شده است. نتایج تحقیق نشان می­دهد که بین نگرش به سودمندی اطلاعات، هنجارهای ذهنی و ریسک درک­ شده با قصد خرید رابطه معناداری وجود دارد.

Community-based influence maximization in social networks under a competitive linear threshold model

The main purpose in influence maximization, which is motivated by the idea of viral marketing in social networks, is to find a subset of key users that maximize influence spread under a certain propagation model. A number of studies have been done over the past few years that try to solve this problem by considering a non-adversarial environment in which there exists only one player with no competitor. However, in real world scenarios, there is always more than one player competing with other players to influence the most nodes. This is called competitive influence maximization. Motivated by this, we try to solve the competitive influence maximization problem by proposing a new propagation model which is an extension of the Linear Threshold model and gives decision-making ability to nodes about incoming influence spread. We also propose an efficient algorithm for finding the influential nodes in a given social graph under the proposed propagation model which exploits the community structure of this graph to compute the spread of each node locally within its own community. The aim of our algorithm is to find the minimum number of seed nodes which can achieve higher spread in comparison with the spread achieved by nodes selected by other competitor. Our experiments on real world and synthetic datasets show that our approach can find influential nodes in an acceptable running time.

A Query Approach for Influence Maximization on Specific Users in Social Networks

Influence maximization is introduced to maximize the profit of viral marketing in social networks. The weakness of influence maximization is that it does not distinguish specific users from others, even if some items can be only useful for the specific users. For such items, it is a better strategy to focus on maximizing the influence on the specific users. In this paper, we formulate an influence maximization problem as query processing to distinguish specific users from others. We show that the query processing problem is NP-hard and its objective function is submodular. We propose an expectation model for the value of the objective function and a fast greedy-based approximation method using the expectation model. For the expectation model, we investigate a relationship of paths between users. For the greedy method, we work out an efficient incremental updating of the marginal gain to our objective function. We conduct experiments to evaluate the proposed method with real-life datasets, and compare the results with those of existing methods that are adapted to the problem. From our experimental results, the proposed method is at least an order of magnitude faster than the existing methods in most cases while achieving high accuracy.