scholarly journals Adaptive Influence Maximization

2021 ◽  
Vol 15 (5) ◽  
pp. 1-23
Author(s):  
Jianxiong Guo ◽  
Weili Wu
Keyword(s):  
Seed Set ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Small Subset ◽  
Influence Spread ◽  
Knapsack Constraint ◽  
Real Scenario ◽  
Submodular Maximization ◽  
Greedy Policy ◽  
Influence Maximization Problem

Influence maximization problem attempts to find a small subset of nodes that makes the expected influence spread maximized, which has been researched intensively before. They all assumed that each user in the seed set we select is activated successfully and then spread the influence. However, in the real scenario, not all users in the seed set are willing to be an influencer. Based on that, we consider each user associated with a probability with which we can activate her as a seed, and we can attempt to activate her many times. In this article, we study the adaptive influence maximization with multiple activations (Adaptive-IMMA) problem, where we select a node in each iteration, observe whether she accepts to be a seed, if yes, wait to observe the influence diffusion process; if no, we can attempt to activate her again with a higher cost or select another node as a seed. We model the multiple activations mathematically and define it on the domain of integer lattice. We propose a new concept, adaptive dr-submodularity, and show our Adaptive-IMMA is the problem that maximizing an adaptive monotone and dr-submodular function under the expected knapsack constraint. Adaptive dr-submodular maximization problem is never covered by any existing studies. Thus, we summarize its properties and study its approximability comprehensively, which is a non-trivial generalization of existing analysis about adaptive submodularity. Besides, to overcome the difficulty to estimate the expected influence spread, we combine our adaptive greedy policy with sampling techniques without losing the approximation ratio but reducing the time complexity. Finally, we conduct experiments on several real datasets to evaluate the effectiveness and efficiency of our proposed policies.

scholarly journals RLIM: Representation Learning Method for Influence Maximization in social networks

2021 ◽  
Author(s):  
Sun Chengai ◽  
Duan Xiuliang ◽  
Qiu Liqing ◽  
Shi Qiang ◽  
Li Tengteng
Keyword(s):  
Network Architecture ◽  
Information Diffusion ◽  
Representation Learning ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Learning Method ◽  
Neural Network Architecture ◽  
Influence Spread ◽  
Information Diffusion Model ◽  
Influence Maximization Problem

Abstract A core issue in influence propagation is influence maximization, which aims to find a group of nodes under a specific information diffusion model and maximize the final influence of this group of nodes. The limitation of the existing researches is that they excessively depend on the information diffusion model and randomly set the propagation ability (probability). Therefore, most of the algorithms for solving the influence maximization problem are basically difficult to expand in large social networks. Another challenge is that fewer researchers have paid attention to the problem of the large difference between the estimated influence spread and the actual influence spread. A measure to solve the influence maximization problem is applying advanced neural network architecture also represents learning method. Based on this idea, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The premise of this algorithm is to construct the influence cascade of each source node. The key is to adopt neural network architecture to realize the prediction of propagation ability. The purpose is to apply the propagation ability to the influence maximization problem by representation learning. Furthermore, the results of the experiments show that RLIM algorithm has greater diffusion ability than the state-of-the-art algorithms on different online social network data sets, and the diffusion of information is more accurate.

scholarly journals A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

A Pagerank-Inspired Heuristic Scheme for Influence Maximization in Social Networks

2015 ◽  
Vol 12 (4) ◽  
pp. 48-62 ◽  
Author(s):  
Bo Zhang ◽  
Yufeng Wang ◽  
Qun Jin ◽  
Jianhua Ma
Keyword(s):  
Social Networks ◽  
Small World ◽  
Real Data ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Small Subset ◽  
Cascade Models ◽  
Comparable Performance ◽  
Law Degree ◽  
Influence Maximization Problem

This article focused on seeking a new heuristic algorithm for the influence maximization problem in complex social networks, in which a small subset of individuals are intentionally selected as seeds to trigger a large cascade of further adoptions of a new behavior under certain influence cascade models. In literature, degree and other centrality-based heuristics are commonly used to estimate the influential power of individuals in social networks. The major issues with degree-based heuristics are twofold. First, those results are only derived for the uniform IC model, in which propagation probabilities on all social links are set as same, which is rarely the case in reality; Second, intuitively, an individual's influence power depends not only on the number of direct friends, but also relates to kinds of those friends, that is, the neighbors' influence should also be taken into account when measuring one's influential power. Based on the general weighted cascade model (WC), this article proposes Pagerank-inspired heuristic scheme, PRDiscount, which explicitly discounts the influence power of those individuals who have social relationships with chosen seeds, to alleviate the “overlapping effect” occurred in behavior diffusion. Then, the authors use both the artificially constructed social network graphs (with the features of power-law degree distribution and small-world characteristics) and the real-data traces of social networks to verify the performance of their proposal. Simulations illustrate that PRDiscount can advantage over the existing degree-based discount algorithm, DegreeDiscount, and achieve the comparable performance as greedy algorithm.

Influence Maximization on Social Networks: A Study

2019 ◽  
Vol 12 ◽  
Author(s):  
Shashank Sheshar Singh ◽  
Kuldeep Singh ◽  
Ajay Kumar ◽  
Bhaskar Biswas
Keyword(s):  
Theoretical Analysis ◽  
Algorithm Design ◽  
Influence Maximization ◽  
Future Research ◽  
Maximization Problem ◽  
Algorithmic Problem ◽  
Influence Spread ◽  
Design Perspective ◽  
Maximization Algorithms ◽  
Influence Maximization Problem

Background: Influence Maximization, which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Objective: In this paper, we give recent studies on influence maximization algorithms. The main goal of this survey is to provide recent studies and future research opportunities. We give taxonomy of influence maximization algorithms with the comparative theoretical analysis. Conclusion: This paper provides a theoretical analysis of influence maximization problem based on algorithm design perspective and also provides the performance analysis of existing algorithms.

scholarly journals Influence maximization in social networks

2019 ◽  
Author(s):  
◽  
Ghinwa Bou Matar
Keyword(s):  
Social Networks ◽  
Seed Set ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Influence Propagation ◽  
Influence Model ◽  
Main Challenge ◽  
Influence Spread ◽  
Total Influence

The main challenge in viral marketing, that is powered by social networks, is to minimize the seed set that will initiate the diffusion process and maximize the total influence at its termination. The aim of this thesis is to study influence propagation models under the influence maximization problem and to investigate the effectiveness of a new model that is based on a multi-objective approach. We propose a Depth-Based Diminishing Influence model (DBDM) that is based on adding nodes to the seed set by considering influenced in-neighbors and how far these in-neighbors are from the initial activated set. As an enhancement to our approach, we used a clustering mechanism to help increase the influence spread. Several experiments were conducted to compare between our approach and previous work. As a result, the selection of the seed set under the DBDM model boosted the influence spread substantially compared to previously proposed models.

scholarly journals A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

scholarly journals No Time to Observe: Adaptive Influence Maximization with Partial Feedback

2017 ◽  
Author(s):  
Jing Yuan ◽  
Shaojie Tang
Keyword(s):  
Intermediate Stage ◽  
Influence Maximization ◽  
Approximation Ratio ◽  
Maximization Problem ◽  
The Past ◽  
Feedback Model ◽  
Adaptive Policy ◽  
Partial Feedback ◽  
Greedy Policy ◽  
Influence Maximization Problem

Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: full-feedback model or zero-feedback model. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose partial-feedback model, which allows us to select a seed at any intermediate stage. We develop a novel alpha-greedy policy that achieves a bounded approximation ratio.

Nonsubmodular Constrained Profit Maximization in Attribute Networks

2021 ◽  
Author(s):  
Liman Du ◽  
Wenguo Yang ◽  
Suixiang Gao
Keyword(s):  
Profit Maximization ◽  
Logistic Function ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Cardinality Constraint ◽  
Three Phase ◽  
Marginal Increment ◽  
The Difference ◽  
Classical Influence ◽  
Influence Maximization Problem

The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.

A survey on meta-heuristic algorithms for the influence maximization problem in the social networks

Computing ◽  
2021 ◽  
Author(s):  
Zahra Aghaee ◽  
Mohammad Mahdi Ghasemi ◽  
Hamid Ahmadi Beni ◽  
Asgarali Bouyer ◽  
Afsaneh Fatemi
Keyword(s):  
Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Maximization Problem ◽  
The Social ◽  
Influence Maximization Problem

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4258-4265
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Linear Threshold ◽  
The Social ◽  
Spread Of Influence ◽  
Influential Users ◽  
High Influence ◽  
Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

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