Optimal strategies for targeted influence in signed networks (original) (raw)
Related papers
Using Social Sensors for Influence Propagation in Networks With Positive and Negative Relationships
IEEE Journal of Selected Topics in Signal Processing, 2015
Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Future network applications can benefit from integrating these structural differences in propagation schemes through socially aware sensors. In this paper, we introduce a propagation model for such social sensor networks with positive and negative relationship types. We tackle two main scenarios based on this model. The first one is to minimize the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing sensor observations about the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as propagation delay, frequency of interaction, the strength of friendship/foe ties or the impact factor of the propagating idea. We next extend this problem by incorporating the impact of message deterioration and ignorance, and by limiting the number of persons influenced against the idea before reaching the target. Second, we study the propagation problem while minimizing the number of negatively influenced persons on the path, and provide extensions to elaborate on the impact of network parameters. We demonstrate our results in both an artificially created network and the Epinions signed network topology. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of network propagation compared to naïve myopic algorithms.
Spreading social influence with both positive and negative opinions in online networks
Big Data Mining and Analytics, 2019
Social networks are important media for spreading information, ideas, and influence among individuals. Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the "word-of-mouth" effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set (MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of Â. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks.
Polarity Related Influence Maximization in Signed Social Networks
PLoS ONE, 2014
Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.
Maximizing the spread of positive influence in signed social networks
Influence maximization in a social network involves identifying an initial subset of nodes with a pre-defined size in order to begin the information diffusion with the objective of maximizing the influenced nodes. In this study, a sign-aware cascade (SC) model is proposed for modeling the effect of both trust and distrust relationships on activation of nodes with positive or negative opinions towards a product in the signed social networks. It is proved that positive influence maximization is NP-hard in the SC model and influence function is neither monotone nor submodular. For solving this NP-hard problem, a particle swarm optimization (PSO) method is presented which applies the random keys representation technique to convert the continuous search space of the PSO to the discrete search space of this problem. To improve the performance of this PSO method against premature convergence, a re-initialization mechanism for portion of particles with poorer fitness values and a heuristic mutation operator for global best particle are proposed. Experiments establish the effectiveness of the SC in modeling the real-world cascades. In addition, PSO method is compared with the well-known algorithms in the literature on two real-world data sets. The evaluation results demonstrate that the proposed method outperforms the compared algorithms significantly in the SC model.
Assessing information diffusion models for influence maximization in signed social networks
Expert Systems With Applications, 2019
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights Two schemes are proposed on distrust propagation in influence maximization. Three information diffusion models are proposed considering trust and distrust. Influence spread is modeled more accurately applying distrust relationships. When a distrusted user performs an action, the target users may tend not to do it.
Communicating in a socially-aware network: Impact of relationship types
2014
Communication networks are linked to and influenced by human interactions. Socially-aware systems should integrate these complex relationship patterns in the network design. This paper studies the impact of friendship and antagonistic relationships between individuals on optimal network propagation policies. We develop a network propagation model for signed networks, and determine the optimal policies to influence a target node with an opinion while minimizing the total number of persons against it. We also provide extensions to this problem to elaborate on the impact of network parameters, such as minimum-delay propagation, while limiting the number of persons influenced against the idea before reaching the target. We provide numerical evaluations in a synthetic setup as well as the Epinions online social dataset. We demonstrate that propagation schemes with social and influence-centric constraints should take into account the relationship types in network design.
Improving the influence under IC-N model in social networks
Discrete Mathematics, Algorithms and Applications, 2015
The influence maximization problem in social networks is to find a set of seed nodes such that the total influence effect is maximized under certain cascade models. In this paper, we propose a novel task of improving influence, which is to find strategies to allocate the investment budget under IC-N model. We prove that our influence improving problem is 𝒩𝒫-hard, and propose new algorithms under IC-N model. To the best of our knowledge, our work is the first one that studies influence improving problem under bounded budget when negative opinions emerge. Finally, we implement extensive experiments over a large data collection obtained from real-world social networks, and evaluate the performance of our approach.
Influence maximization, defined by Kempe, , is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1 − 1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of Θ( n/k) on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to com- This version is a revision following the SDM camera-ready version. pute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.
An Analytical Model for the Propagation of Social Influence
2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
Studying the propagation of social influence is critical in the analysis of online social networks. While most existing work focuses on the expected number of users influenced, the detailed probability distribution of users influenced is also significant. However, determining the probability distribution of the final influence propagation state is difficult. Monte-Carlo simulations may be used, but are computationally expensive. In this paper, we develop an analytical model for the influence propagation process in online social networks based on discretetime Markov chains, and deduce a closed-form equation for the n-step transition probability matrix. We show that given any initial state, the probability distribution of the final influence propagation state may be easily obtained from a matrix product. This provides a powerful tool to further understand social influence propagation.