Social Influence Maximization Problem Research Articles

Product Adoption Maximization Leveraging Social Influence and User Interest Mining

A Social Networking Service (SNS) platform provides digital footprints to discover users’ interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users’ relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users’ interest and social influence, where the user interest model relies on historical user behavior and the seed users’ evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users’ past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

A Node Embedding-Based Influential Spreaders Identification Approach

Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node identification, in both calculation and richer application scope. In this paper, we propose a two-step node embedding-based solution for the social influence maximization problem (IMP). The solution employs a revised network-embedding algorithm to map input nodes into vector space in the first step. In the second step, the solution clusters the vector space nodes into subgroups and chooses the subgroups’ centers to be the influential spreaders. The proposed approach is a simple but effective IMP solution because it takes both the social reinforcement and homophily characteristics of the social network into consideration in node embedding and seed spreaders selection operation separately. The information propagation simulation experiment of single-point contact susceptible-infected-recovered (SIR) and full-contact SIR models on six different types of real network data sets proved that the proposed social influence maximization (SIM) solution exhibits significant propagation capability.

A survey on influence maximization in a social network

Given a social network with diffusion probabilities as edge weights and a positive integer k, which k nodes should be chosen for initial injection of information to maximize the influence in the network? This problem is popularly known as the Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain, since one and half decades or so. Due to its practical importance in various domains, such as viral marketing, target advertisement and personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. This paper presents a survey on the progress in and around SIM Problem. At last, it discusses current research trends and future research directions as well.

Social Influence Maximization in Hypergraph in Social Networks

Crowd psychology plays an important role in determining the kind of activities that a person performs. In reality, in a social network, crowd influence has been observed and it cannot be ignored when considering information diffusion problems. In this paper, we model crowd influence as a hyperedge e = (H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> , v) with weight 0 ≤ P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ≤ 1, where H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> is the head node set and v is the tail node, means v will be activated by H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> with probability P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> only after each node in H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> is activated. Then, the Social Influence Maximization Problem in Hypergraph (SIMPH) aims to select k initially-influenced seed users in a directed hypergraph G = (V, E, P). The objective is to maximize the expected number of eventually-influenced users. We show that SIMPH is NP-hard and the objective function is neither submodular nor supermodular. We develop a lower bound and an upper bound that are submodular. We prove that maximizing these two bounds are still NP-hard under IC model. Then, we present a D-SSA algorithm for general weighted social influence maximization problem preserving (1 - 1=e - ε)-approximation. We formulate a sandwich approximation framework, which preserves a theoretical analysis result. Finally, we evaluate our algorithm on real world data sets. The results show the effectiveness and the efficiency of the proposed algorithm.

Maximizing the earned benefit in an incentivized social networking environment: a community-based approach

Given a social network of users represented as a directed graph with edge weight as diffusion probability, the Social Influence Maximization Problem asks for selecting a set of highly influential users for initial activation to maximize the influence in the network. In this paper, we study a variant of this problem, where nodes are associated with a selection cost signifying the incentive demand; a fixed budget is allocated for the seed set selection process; a subset of the nodes is designated as the target nodes, and each of them is associated with a benefit value that can be earned by influencing the corresponding target user; and the goal is to choose a seed set within the allocated budget for maximizing the earned benefit. Formally, we call this problem as the Earned Benefit Maximization in Incentivized Social Networking Environment or Earned Benefit Maximization Problem (EBM Problem), in short. For this problem, we develop a priority-based ranking methodology having three steps. First, marking the effective nodes for the given target nodes; second, priority computation of the effective nodes and the third is to choose the seed nodes based on this priority value within the budget. We implement the proposed methodology with two publicly available social network datasets and observe that the proposed methodology can achieve more benefit compared to the baseline methods. To improve the proposed methodology, we exploit the community structure of the network. Experimental results show that the incorporation of community structure helps the proposed methodology to achieve more benefit without much increase in computational burden.

Finding influentials in social networks using evolutionary algorithm

The social influence maximization problem is an important research topic for many years since it has a tremendous impact on society. As social influence can be maximized for many purposes, such as marketing, politics, spreading innovations, there are many stakeholders interested in progress in this area. As it has been shown, for most settings finding an optimal seed set is an NP-hard problem, this is why various heuristics started to emerge since the statement of the problem. In this work, we explore the applicability of evolutionary algorithm for influence maximization. The experiments conducted using real and artificial social networks and the linear threshold influence model show that this approach offers not only speed and accuracy. Also, some other interesting features have been found, such as the transferability of its parameters to other datasets. Summarizing the results, it was observed that the evolutionary algorithm does not lose its performance when limiting its time by the factor of two and for most datasets, we obtained a high correlation of ranked parameters’ sets used for the evolutionary algorithm, typically around 0.9. Overall, these features combined make this approach an interesting research direction for influence maximization.

Whom should I persuade during a negotiation? An approach based on social influence maximization

During a negotiation, an agent must make several key decisions in order to achieve a profitable agreement. When the negotiation is carried out in a social context, agents can use persuasion, besides the traditional exchange of concessions. To carry out the persuasion and make concessions, the agents must employ resources that are usually scarce. For this reason, the agents should carefully decide which opponent they should persuade to maximise their profit, especially when the negotiation involves multiple parties. To make this decision, we propose that the agents should persuade the opponents with a high influence on the other agents involved in the negotiation. Therefore, we represent a negotiation context as a social influence maximization problem and solve it under a model that learns how influence flows in a network by analyzing historical information. This allows an agent to determine what opponents exert the highest influence. Finally, the agent uses this information to decide which opponent to persuade during the negotiation. Experimental results showed that the agreement rate increased when agents applied this approach.

Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing “Word-of-Mouth” effect in social networks. Time plays an important role in the influence spread from one user to another and the time needed for a user to influence another varies. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm. To improve the algorithm scalability, we propose the concept of Influence Spreading Path in social networks and develop a set of new algorithms for the time constrained influence maximization problem. We further parallelize the algorithms for achieving more time savings. Additionally, we generalize the proposed algorithms for the conventional influence maximization problem without time constraints. All of the algorithms are evaluated over four public available datasets. The experimental results demonstrate the efficiency and effectiveness of the algorithms for both conventional influence maximization problem and its time constrained version.

A novel approach to online social influence maximization

Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.