A quick GRASP-based method for influence maximization in social networks

AbstractThe evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an $$\mathcal {NP}$$ NP -hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.

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

Future Internet ◽

10.3390/fi11040095 ◽

2019 ◽

Vol 11 (4) ◽

pp. 95

Author(s):

Wang ◽

Zhu ◽

Liu ◽

Wang

Keyword(s):

Social Networks ◽

Social Network ◽

Memory Effect ◽

Influence Maximization ◽

Viral Marketing ◽

Maximization Problem ◽

Social Reinforcement ◽

Reinforcement Effect ◽

The Social ◽

Influence Maximization Problem

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.

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Influence Maximization in Social Networks using Deterministic Crowding Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k2448.0981119 ◽

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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Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks

Sensors ◽

10.3390/s21030709 ◽

2021 ◽

Vol 21 (3) ◽

pp. 709

Author(s):

Khurshed Ali ◽

Cheng-Te Li ◽

Yi-Shin Chen

Keyword(s):

Social Networks ◽

Social Network ◽

Maximization Problem ◽

Target Region ◽

Specific Location ◽

Influential Users ◽

Location Based Social Networks ◽

Location Type ◽

Selection Of ◽

Product Promotion

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location’s popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

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

10.36227/techrxiv.13153139 ◽

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. 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). 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. 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.

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Positive Influence Maximization in the Signed Social Networks Considering Polarity Relationship and Propagation Probability

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194021500078 ◽

2021 ◽

Vol 31 (02) ◽

pp. 249-267

Author(s):

Liqing Qiu ◽

Shuang Zhang ◽

Jinfeng Yu

Keyword(s):

Social Networks ◽

Seed Set ◽

Positive Influence ◽

Vital Role ◽

Influence Maximization ◽

Viral Marketing ◽

Propagation Model ◽

Maximization Problem ◽

Signed Social Networks ◽

Independent Cascade

The purpose of influence maximization problem is to select a small seed set to maximize the number of nodes influenced by the seed set. For viral marketing, the problem of influence maximization plays a vital role. Current works mainly focus on the unsigned social networks, which include only positive relationship between users. However, the influence maximization in the signed social networks including positive and negative relationships between users is still a challenging issue. Moreover, the existing works pay more attention to the positive influence. Therefore, this paper first analyzes the positive maximization influence in the signed social networks. The purpose of this problem is to select the seed set with the most positive influence in the signed social networks. Afterwards, this paper proposes a model that incorporates the state of node, the preference of individual and polarity relationship, called Independent Cascade with the Negative and Polarity (ICWNP) propagation model. On the basis of the ICWNP model, this paper proposes a Greedy with ICWNP algorithm. Finally, on four real social networks, experimental results manifest that the proposed algorithm has higher accuracy and efficiency than the related methods.

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Community-based framework for influence maximization problem in social networks

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i3.pp1604-1609 ◽

2021 ◽

Vol 24 (3) ◽

pp. 1604

Author(s):

Mustafa K. Alasadi ◽

Ghusoon Idan Arb

Keyword(s):

Community Structure ◽

Social Network ◽

Diffusion Process ◽

Influence Maximization ◽

Viral Marketing ◽

Community Diversity ◽

Maximization Problem ◽

Special Role ◽

The Social ◽

Influence Maximization Problem

Given a social graph, the influence maximization problem (IMP) is the act of selecting a group of nodes that cause maximum influence if they are considered as seed nodes of a diffusion process. IMP is an active research area in social network analysis due to its practical need in applications like viral marketing, target advertisement, and recommendation system. In this work, we propose an efficient solution for IMP based on the social network structure. The community structure is a property of real-world graphs. In fact, communities are often overlapping because of the involvement of users in many groups (family, workplace, and friends). These users are represented by overlapped nodes in the social graphs and they play a special role in the information diffusion process. This fact prompts us to propose a solution framework consisting of three phases: firstly, the community structure is discovered, secondly, the candidate seeds are generated, then lastly the set of final seed nodes are selected. The aim is to maximize the influence with the community diversity of influenced users. The study was validated using synthetic as well as real social network datasets. The experimental results show improvement over baseline methods and some important conclusions were reported.

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Influence maximization in social networks

10.26756/th.2020.163 ◽

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.

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

10.36227/techrxiv.13153139.v1 ◽

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

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On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Complexity ◽

10.1155/2017/5049836 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Xiaodong Liu ◽

Xiangke Liao ◽

Shanshan Li ◽

Si Zheng ◽

Bin Lin ◽

...

Keyword(s):

Social Networks ◽

Real World ◽

Search Space ◽

Influence Maximization ◽

Viral Marketing ◽

Maximization Problem ◽

Dynamic Social Networks ◽

Influence Spread ◽

The Impact ◽

Influential Individuals

Influence maximization problem aims to identify the most influential individuals so as to help in developing effective viral marketing strategies over social networks. Previous studies mainly focus on designing efficient algorithms or heuristics on a static social network. As a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time. In this paper, we propose an incremental approach, IncInf, which can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to narrow the search space into nodes experiencing major increases or with high degrees. To evaluate the efficiency and effectiveness, we carried out extensive experiments on real-world dynamic social networks: Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static algorithm, IncInf achieves remarkable speedup in execution time while maintaining matching performance in terms of influence spread.

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