Targeted Influence Maximization Research Articles

A privacy‐sensitive data identification model in online social networks

AbstractPrivacy protection in online social networks (OSNs) has received a great deal of attention in recent years. One way of circumventing conventional privacy protection is privacy inference based on data that can be easily obtained in OSNs. Previous work on privacy inference has studied the issue mostly from the viewpoint of the attackers and methods thus designed were mostly aimed at pursuing the accuracy of the inference results with little regard on the causes of privacy breaches. To develop more effective privacy protection mechanisms that takes privacy inference into consideration, it is necessary to identify the information that plays a more important role in privacy breaches. In this paper, we propose a privacy‐sensitive data identification model in OSNs, which can identify key pieces of data that are most sensitive as far the privacy of the user is concerned. Firstly, a privacy inference method is proposed based on conditional random fields to infer the privacy of the target users. Then, a privacy‐sensitive data identification method is proposed by using the intermediate data of the proposed privacy inference method based on the targeted influence maximization algorithm. Thus, the key pieces of data in the form of user attributes and relationships on which the privacy of the target user depends can be determined to facilitate the implementation of privacy protection mechanisms. The effectiveness as well as the advantages of the proposed model is verified and demonstrated through experiment using real datasets. The impact of the key factors on privacy inference is also analyzed to guide the design of effective privacy protection strategies.

Targeted influence maximization in complex networks

Many real-world applications based on spreading processes in complex networks aim to deliver information to specific target nodes. However, it remains challenging to optimally select a set of spreaders to initiate the spreading process. In this paper, we study the targeted influence maximization problem using a susceptible–infected–recovered (SIR) model as an example. Formulated as a combinatorial optimization, the objective is to identify a given number of spreaders that can maximize the influence over target nodes while minimize the influence over non-target nodes. To find a practical solution to this optimization problem, we develop a theoretical framework based on a message passing process and perform a stability analysis on the equilibrium solution using non-backtracking (NB) matrices. Based on the optimal perturbation on the equilibrium solution, we further introduce a metric, termed targeted collective influence, for each node to identify influential spreaders for targeted spreading processes. The proposed method, validated in both synthetic and real-world networks, outperforms other competing heuristic approaches. Our results provide a framework for analyzing the targeted influence maximization problem and a practical method to identify spreaders in real-world applications.

Targeted influence maximization in competitive social networks

Advertising using the word-of-mouth effect is quite effective in promoting products. In the last decade, there has been intensive research studying the influence maximization problem in marketing. The problem of influence maximization aims to identify a small group of people in the social network as seeds such that eventually, they will trigger the largest influence spread or product adoption in the network. In practical scenarios of online marketing, it is common that there are competitions among similar products in the network and the promotion is targeted at specific groups of users. For instance, an event organizer disseminates an event ad on a social platform hoping to attract attention of the most number of local residents. Meanwhile, there are multiple competing events being promoted on the social platform. In this paper, we formulate such problem as Targeted Influence Maximization in Competitive social networks (TIMC). To model the influence diffusion, we combine the target nodes and competitive relationships into an independent cascade model. We propose a Reverse Reachable set-based Greedy (RRG) algorithm to solve the TIMC problem and theoretically proved its approximation ratio. We also design a pruning strategy to further speed up the performance of the proposed algorithm. Extensive experiments have confirmed the efficiency of the proposed RRG algorithm. We also find that the algorithm works particularly well for sparse large networks with strong competition.

Efficient Targeted Influence Maximization Based on Multidimensional Selection in Social Networks

The influence maximization problem over social networks has become a popular research problem, since it has many important practical applications such as online advertising, virtual market, and so on. General influence maximization problem is defined over the whole network, whose intuitive aim is to find a seed node set with size at most k in order to affect as many as nodes in the network. However, in real applications, it is commonly required that only special nodes (target) in the network are expected to be influenced, which can use the same cost of placing seed nodes but influence more targeted nodes really needed. Some research efforts have provided solutions for the corresponding targeted influence maximization problem (TIM for short). However, there are two main drawbacks of previous works focusing on the TIM problem. First, some works focusing on the case the targets are given arbitrarily make it hard to achieve efficient performance guarantee required by real applications. Second, some previous works studying the TIM problems by specifying the target set in a probabilistic way is not proper for the case that only exact target set is required. In this paper, we study the Multidimensional Selection based Targeted Influence Maximization problem, MSTIM for short. First, the formal definition of the problem is given based on a brief and expressive fragment of general multi-dimensional queries. Then, a formal theoretical analysis about the computational hardness of the MSTIM problem shows that even for a very simple case that the target set specified is 1 larger than the seed node set, the MSTIM problem is still NP-hard. Then, the basic framework of RIS (short for Reverse Influence Sampling) is extended and shown to have a 1 − 1/e − ϵ approximation ratio when a sampling size is satisfied. To satisfy the efficiency requirements, an index-based method for the MSTIM problem is proposed, which utilizes the ideas of reusing previous results, exploits the covering relationship between queries and achieves an efficient solution for MSTIM. Finally, the experimental results on real datasets show that the proposed method is indeed rather efficient.

A discrete genetic learning enabled PSO for targeted positive influence maximization in consumer review networks

A consumer review network (CRN) is a social network among the members of a consumer review website where the relationships are formed based on the mutual ratings of the existing reviews of the participating members. The relationship is positive (negative) i.e. trust-based (distrust-based) when most of the reviews are given high (low) ratings. In a CRN, the consumers may not be interested in all product categories. The influence maximization in such a network demands seed set selection in such a way that the number of influenced consumers (with interest in the advertised product category) will be maximum. Formally, this is referred to as the targeted influence maximization (TIM) problem. Moreover, as the CRN is treated as a signed social network, the polarity of the social relationships impacts the influence propagation. As per the present state of the art, none of the existing solutions for TIM have considered the network as a signed one and are thus not suitable for CRN. In this paper, a metaheuristic discrete genetic-learning-enabled particle swarm optimization algorithm combined with a trustworthiness-heuristic-based local search strategy has been proposed for targeted positive influence maximization in CRNs. The existing spread estimation function has been replaced by a computationally efficient positive influence spread estimation function. The experiment has been conducted on two real-life CRNs and compared with the existing notable algorithm for necessary validation of the proposed solution.

Attribute based diversification of seeds for targeted influence maximization

Embedding diversity into knowledge discovery is important: the patterns mined will be more novel, more meaningful, and broader. Surprisingly, in the classic problem of influence maximization in social networks, relatively little study has been devoted to diversity and its integration into the objective function of an influence maximization method.In this work, we propose the integration of a categorical-based notion of seed diversity into the objective function of a targeted influence maximization problem. In this respect, we assume that the users of a social network are associated with a categorical dataset where each tuple expresses the profile of a user according to a predefined schema of categorical attributes. Upon this assumption, we design a class of monotone submodular functions specifically conceived for determining the diversity of the subset of categorical tuples associated with the seed users to be discovered. This allows us to develop an efficient approximate method, with a constant-factor guarantee of optimality. More precisely, we formulate the attribute-based diversity-sensitive targeted influence maximization problem under the state-of-the-art reverse influence sampling framework, and we develop a method, dubbed ADITUM, that ensures a (1-1/e-∊)-approximate solution under the general triggering diffusion model. Extensive experimental evaluation based on real-world networks as well as synthetically generated data has shown the meaningfulness and uniqueness of our proposed class of set diversity functions and of the ADITUM algorithm, also in comparison with methods that exploit numerical-attribute-based diversity and topology-driven diversity in influence maximization.

Targeted influence maximization under a multifactor-based information propagation model

Information propagation modeling and influence maximization are two important research problems in viral marketing. When marketing information is given, how can the seed nodes be efficiently identified to maximize the spread of the information through the network? To answer this question, we consider multiple factors in information propagation, such as information content, social influence and user authority, and propose a multifactor-based information propagation model (MFIP). Then, we utilize the first-order influence of the nodes to approximate their influence and propose an efficient heuristic algorithm named weighted degree decrease (WDD) to select the seed nodes under the MFIP model. Experimental evaluations with four real-world social network datasets demonstrate the effectiveness and efficiency of our algorithm.

Targeted Influence Maximization Based on Cloud Computing Over Big Data in Social Networks

This paper focuses on the targeted influence maximization based on cloud computing in social networks. Most of existing influence maximization works assume that the influence diffusion probabilities on edges are fixed, and identify the Top-k users to maximize the spread of influence assuming the knowledge of the entire network graph. However, in real-world scenarios, edge probabilities are typically different based on various topics, and may be affected by information received. Meanwhile, obtaining complete network data is difficult due to privacy and computational considerations. Moreover, existing influence maximization algorithms considering target users do not discuss cloud computing which lead to low computational efficiency when dealing with big datasets in social networks. To this end, this paper proposes a targeted influence maximization solution based on cloud computing. First, a new topic-aware model called tag-aware IC model is presented, which takes into account users' interests, characteristics of the item being propagated, and the similarity between users and the related information. Then, efficient algorithms with approximation guarantee are provided using a bounded number of queries to the graph structure. These methods aim to find a seed set that maximizes the expected influence spread over target users who are relevant to given topics. Finally, empirical studies of the proposed algorithms are designed and performed on real datasets. The experimental results show that the techniques in this paper achieves speedup and savings in storage compared with the state-of-the-art methods.

Topology-Driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks

Research on influence maximization has often to cope with marketing needs relating to the propagation of information towards specific users. However, little attention has been paid to the fact that the success of an information diffusion campaign might depend not only on the number of the initial influencers to be detected but also on their diversity w.r.t. the target of the campaign. Our main hypothesis is that if we learn seeds that are not only capable of influencing but also are linked to more diverse (groups of) users, then the influence triggers will be diversified as well, and hence the target users will get higher chance of being engaged. Upon this intuition, we define a novel problem, named Diversity-sensitive Targeted Influence Maximization (DTIM), which assumes to model user diversity by exploiting only topological information within a social graph. To the best of our knowledge, we are the first to bring the concept of topology-driven diversity into targeted IM problems, for which we define two alternative definitions. Accordingly, we propose approximate solutions of DTIM, which detect a size-k set of users that maximizes the diversity-sensitive capital objective function, for a given selection of target users. We evaluate our DTIM methods on a special case of user engagement in online social networks, which concerns users who are not actively involved in the community life. Experimental evaluation on real networks has demonstrated the meaningfulness of our approach, also highlighting the opportunity of further development of solutions for DTIM applications.

Location‐aware targeted influence maximization in social networks

In this paper, we study the location‐aware targeted influence maximization problem in social networks, which finds a seed set to maximize the influence spread over the targeted users. In particular, we consider those users who have both topic and geographical preferences on promotion products as targeted users. To efficiently solve this problem, one challenge is how to find the targeted users and compute their preferences efficiently for given requests. To address this challenge, we devise a TR‐tree index structure, where each tree node stores users' topic and geographical preferences. By traversing the TR‐tree in depth‐first order, we can efficiently find the targeted users. Another challenge of the problem is to devise algorithms for efficient seeds selection. We solve this challenge from two complementary directions. In one direction, we adopt the maximum influence arborescence (MIA) model to approximate the influence spread, and propose two efficient approximation algorithms with approximation ratio, which prune some candidate seeds with small influences by precomputing users' initial influences offline and estimating the upper bound of their marginal influences online. In the other direction, we propose a fast heuristic algorithm to improve efficiency. Experiments conducted on real‐world data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

Real-time targeted influence maximization for online advertisements

Advertising in social network has become a multi-billion-dollar industry. A main challenge is to identify key influencers who can effectively contribute to the dissemination of information. Although the influence maximization problem, which finds a seed set of k most influential users based on certain propagation models, has been well studied, it is not target-aware and cannot be directly applied to online advertising. In this paper, we propose a new problem, named Keyword-Based Targeted Influence Maximization (KB-TIM), to find a seed set that maximizes the expected influence over users who are relevant to a given advertisement. To solve the problem, we propose a sampling technique based on weighted reverse influence set and achieve an approximation ratio of (1 − 1/ e − ε ). To meet the instant-speed requirement, we propose two disk-based solutions that improve the query processing time by two orders of magnitude over the state-of-the-art solutions, while keeping the theoretical bound. Experiments conducted on two real social networks confirm our theoretical findings as well as the efficiency. Given an advertisement with 5 keywords, it takes only 2 seconds to find the most influential users in a social network with billions of edges.