Influential Users In Social Networks Research Articles

Fuzzy Influence Maximization in Social Networks

Influence maximization is a fundamental problem in social network analysis. This problem refers to the identification of a set of influential users as initial spreaders to maximize the spread of a message in a network. When such a message is spread, some users may be influenced by it. A common assumption of existing work is that the impact of a message is essentially binary: A user is either influenced (activated) or not influenced (non-activated). However, how strongly a user is influenced by a message may play an important role in this user’s attempt to influence subsequent users and spread the message further; existing methods may fail to model accurately the spreading process and identify influential users. In this article, we propose a novel approach to model a social network as a fuzzy graph where a fuzzy variable is used to represent the extent to which a user is influenced by a message (user’s activation level). By extending a diffusion model to simulate the spreading process in such a fuzzy graph, we conceptually formulate the fuzzy influence maximization problem for which three methods are proposed to identify influential users. Experimental results demonstrate the accuracy of the proposed methods in determining influential users in social networks.

Exploring the attributes of influential users in social networks using association rule mining

Exploring the attributes of influential users in social networks using association rule mining

Graph-based modeling using association rule mining to detect influential users in social networks

Information diffusion is an important and attractive field of research in the area of social network analysis, and is at the heart of many studies and applications of knowledge extraction and prediction. Most of these studies have focused on identifying the most influential users and predicting user participation. Nevertheless, despite the extensive research efforts that have been made to tackle these issues, there is still a need for approaches based on association rules mining and graph theory. In this study, we contribute to research in this field by introducing a novel graph-based approach that applies association rules mining to detect influential users. We argue that users influence each other, and that it is possible to predict a user’s interests and participation based on previous interactions in the social network. We introduce new concepts and algorithms for more efficient characterization of influential users, and develop an effective approach for the discovery of influencers by using association rule techniques to extract the hidden relationships between users. To evaluate the feasibility and effectiveness of our approach, we propose a new centrality measure called the completeness centrality, and perform an evaluation based on a case study selected from the literature. We then evaluate the effectiveness of the proposed centrality measure by using the susceptible-infected-recovered model and the overlapping similarity measure. The results demonstrate that our measure is feasible and effective for use in identifying influential spreaders, based on a comparison with existing centrality measures such as degree, betweenness, closeness, and eigenvector methods. Finally, to illustrate the efficiency of our approach, experiments were run on 25 generated diffusion graphs, and the results showed that our approach could achieve a high level of performance in terms of computational time for large-scale networks.

LKG: A fast scalable community-based approach for influence maximization problem in social networks

The detection of top influential users in social networks is considered one of the current vital research field. The spreading of the information in social networks can be analyzed and sometimes controlled by studying those top influential users. This paper proposes LKG, a fast and scalable hybrid approach to detect top influential users in social networks, suitable for both directed and undirected networks. The LKG hybrid approach consists of three phases: (1) community detection, in which the complete social network is partitioned into related communities using the Louvain algorithm; (2) detection of community top nodes by applying the k-shell decomposition locally in each portioned community; and (3) selection generalization, in which the prior obtained results are generalized for maximizing the spread of influence. Experimental studies were conducted on several datasets with different sizes. The results have been shown to achieve better results for the spread of influence using incomplete social networks than the existing related work models and with far much less processing time.

Finding influential users in microblogs: state-of-the-art methods and open research challenges

ABSTRACT Social networks are online platforms that people use for interaction, information sharing and propagation of new ideas. Finding influential users in online social networks is a significant research problem due to its vast research applications in information diffusion, marketing and advertising. The relevant literature presents several models proposed for identifying influential users in social networks. In this survey, we present a review of the most relevant studies on influential users mining in microblog networks. First, we propose a new taxonomy by classifying the influence finding algorithms into five main categories based on their underlying framework and baseline methods. Second, each study is analysed according to the proposed framework, experimental datasets, validation approaches and evaluation results. Finally, the survey concludes with discussion on applications from the relevant literature, exploring open research challenges and presenting possible future research directions. The findings of this survey indicate that influential users mining in microblogs has many applications in marketing, advertising and information diffusion. In addition, this survey can be used as a guideline, particularly by young researchers, for establishing a baseline before initiating a research or identifying attractive as well as relevant research insights.

Novel Influence Maximization Algorithm for Social Network Behavior Management

The users largely contributing towards product adoption or information utilization in social networks are identified by the process of influence maximization. The exponential growth in social networks imposes several challenges in the analyses of these networks. Important has been given to modeling structural properties while the relationship between users and their social behavior has being ignored in the existing literature. With respect to the social behavior, the influence maximization task has been parallelized in this paper. In order to maximize the influence in social networks, an interest based algorithm with parallel social action has been proposed. This is algorithm enables identifying influential users in social network. The interactive behavior of the user is weighted dynamically as social actions along with the interests of the users. These two semantic metrics are used in the proposed algorithm. An optimal influential nodes set is computed by implementing the machines with CPU architecture with perfect parallelism through community structure. This helps in reducing the execution time and overcoming the real-word social network size challenges. When compared to the existing schemes, the proposed algorithm offers improved efficiency in the calculation speed on real world networks.

Parallel social behavior-based algorithm for identification of influential users in social network.

Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is prone to grow exponentially, which makes it difficult to analyze. Critically, most of approaches in the literature focus only on modeling structural properties, ignoring the social behavior in the relations between users. For this, we tend to parallelize the influence maximization task based on social behavior. In this paper, we introduce a new parallel algorithm, named PSAIIM, for identification of influential users in social network. In PSAIIM, we uses two semantic metrics: the user’s interests and the dynamically-weighted social actions as user interactive behaviors. In order to overcome the size of actual real-world social networks and to minimize the execution time, we used the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes. Experimental results on real-world networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms, especially in the speed of calculation.

Maximizing positive influence in competitive social networks: A trust-based solution

Online social networks provide convenience for users to propagate ideas, products, opinions, and many other items that compete with different items for influence spread. How to accurately model the spread of competitive influence is still a challenging problem. Almost all reported methods ignore the effect of trust relationships in the spread of competitive influence. Maximizing competitive influence aims to detect the top-k positive or negative influential users in social networks with competing cascades. However, finding an optimal solution to this problem is NP-hard. This study focuses on exploring the above three issues by devising a trust-based solution. First, we established a new model of trust-based competitive influence diffusion that simulates the spread of positive and negative influence. Second, we estimated trust values via generalized network flows and used these values to calculate influence probabilities. Finally, we developed an efficient algorithm of trust-based competitive influence maximization through a heuristic pruning method. Extensive comparisons have been conducted on synthetic and real-world datasets. The effectiveness and efficiency of our approach are verified by analyzing the spread of competitive influence and the time complexity of detecting seed sets. Moreover, our approach is more practical than other baselines on real-world social networks.

Efficient algorithms based on centrality measures for identification of top-K influential users in social networks

The problem of influence maximization has grown in popularity and interest in recent decades due to its valuable application in various fields. This problem mainly focuses on identifying Top-K influential users that, when selected, the influence spread will be maximized. Thus, the identification of such nodes is pivotal in increasing the adoption of promoted information and behavior within the network. In this paper, we propose two new efficient algorithms, namely, “MinCDegKatz d-hops (MaxCDegKatz d-hops)” that relies on a combination of centralization measures due to their known efficiency and performance in terms of influence spread and their low runtime complexity. The proposed algorithms combine between the degree centrality as local measure and the Katz centrality as global centrality metric on a graph with preselected weight edges that should exceed a predefined threshold value. Thus, each selected seed set is separated by a number of hops that differ depending on the graph radius. Then, the influence spread is measured for the two proposed algorithms under the Independent Cascade (IC) and Linear Threshold (LT) models. We conducted extensive experiments on a large-scale graph that demonstrated the performance of our proposed algorithms against existing approaches in term of spreading ability and time complexity.

Influence propagation: Interest groups and node ranking models

Influence propagation is studied in various contexts with significant practical potential applications such as viral marketing, monitoring people opinions, social psychology analysis and communities discovery. All the previously mentioned applications are concerned about the role played by the user in social network and his/her effect on other users. The current literature lacks approaches that identify influential users in social networks and analyze users ranking with respect to the users interactivity to the disseminated content.The main contribution of this work is to achieve users ranking based on influence propagation in social networks. In order to achieve this goal, two models are proposed. The first model captures interest groups regarding specific disseminated content. The second model is a novel influence propagation model that ranks users in each interest group based on their role in spreading content. Moreover, this model introduces the new concept of ”ultimate observers” to adjust the rank of influential users in each group. Finally, we perform extensive experiments on real datasets to demonstrate the relevance of the proposed models.Both models are evaluated in experimental setup using the following benchmark datasets: Highschool, Email-Eu-core, US Airports, Advogato Trust and Twitter Lists networks. The proposed models are assessed in respect of the accurate separation of interest groups, distinction, uniqueness and effectiveness of nodes ranking.Experiments show that the proposed models have promising results in detecting the interest groups and ranking users in terms of their influence propagation.

Finding influential users in social networks based on novel features & link-based analysis

The social web appears to enrich human lives by providing effective applications for online social interactions. Microblogs are one of the most important applications of the social Web. The Microbloggers who influence the social community users through their content in the form of tweets are known as the influential microbloggers. The identification of such influential microbloggers has vast applications in advertising, online marketing, corporate communication, information dissemination, etc. This paper investigates the problem of identifying influential microbloggers by proposing MIPPLA (Model to identify Influential using Productivity, Popularity and Link Analysis) model which integrates the modules of Productivity and Popularity. The Productivity module considers a micro-blogger’s activity and the Popularity module identifies a microbloggers influence in an online social community. In addition, we modify the classic PageRank by utilizing the Twitter features such as retweet, mention, and reply for ranking the influential users. The proposed approaches are evaluated using real-world social networks. The results prove that the MIPPLA model efficiently identifies and ranks the top influential users in an effective manner as compared to the existing techniques.

Identification of influential users in social network using gray wolf optimization algorithm

A challenging issue in viral marketing is to effectively identify a set of influential users. By sending the advertising messages to this set, one can reach out the largest area of the network. In this paper, we formulate the influence maximization problem as an optimization problem with cost functions as the influentiality of the nodes and the distance between them. Maximizing the distance between the seed nodes guarantees reaching to different parts of the network. We use gray wolf optimization algorithm to solve the problem. Our experimental results on three real-world networks show that proposed method outperforms state-of-the-art influence maximization algorithms. Furthermore, it has lower computational time than other meta-heuristic methods.

Identification of influential users in social networks based on users’ interest

Ever increasing popularity social networks has attracted many companies to use them for viral marketing. Identification of influential users for spreading news and marketing is a major challenge in viral marketing. In most of the existing influence maximization approaches, the topological locations of nodes on the network has been considered as a criterion to determine their influentiality. They have mainly neglected users’ interest in the marketing message. Although a number of existing works have considered interests of users, they have not used any criterion to specify the interest. This manuscript proposes a novel criterion to measure the interest of users in the marketing messages. We then propose a novel algorithm to obtain the set of the most influential users. Experimental results on real-world and synthetic networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms.

Influence maximization in large social networks: Heuristics, models and parameters

Online social networks play a major role not only in socio psychological front, but also in the economic aspect. The way social network serves as a platform of information spread, has attracted a wide range of applications at its doorstep. In recent years, lot of efforts are directed to use the phenomenon of vast spread of information, via social networks, in various applications, ranging from poll analysis, product marketing, identifying influential users and so on. One such application that has gained research attention is the influence maximization problem. The influence maximization problem aims to fetch the top influential users in the social networks. The aim of the paper is to provide a comprehensive analysis on the state of art approaches towards identifying influential users. In this review, we discuss various challenges and approaches to identify influential users in online social networks. This review concludes with future research direction, helping researchers to bring possible improvements to the existing body of work.

A robust method to discover influential users in social networks

Identifying the influential spreaders is an important issue in understanding and controlling the spreading processes in social networks. The key to the influential spreader identification problem is how to evaluate the spreading ability of the nodes. Centralities such as degree, semi-local centrality, betweenness, closeness, k-shell are usually used as evaluation metric. However, we observe that these centralities are sensitive not only to the spreading probability, but also to the network structure. We are not sure which centrality is efficient when we face a new network. In this paper, we propose a robust method named Adjustable multi-Hops Spreading (AHS). In AHS, we refine the influence of a node into direct influence and indirect influence, and then integrate them with a adjustable parameter. The experimental results in both real social networks and artificial networks show that AHS outperforms other centralities in effectiveness, robustness and the distinguish ability.

Identification of influential users by neighbors in online social networks

Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors’ role for each user, a two-level method is proposed for both detecting users’ influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects.

Identifying Influential Users in Social Networks

Identifying Influential Users in Social Networks

Finding top-k influential users in social networks under the structural diversity model

The influence maximization problem in a large-scale social network is to identify a few influential users such that their influence on the other users in the network is maximized, under a given influence propagation model. One common assumption adopted by two popular influence propagation models is that a user is more likely to be influenced if more his/her friends have already been influenced. This assumption recently however was challenged to be over simplified and inaccurate, as influence propagation process typically is much more complex than that, and the social decision of a user depends more subtly on the network structure, rather than how many his/her influenced friends. Instead, it has been shown that a user is very likely to be influenced by structural diversities of his/her friends. In this paper, we first formulate a novel influence maximization problem under this new structural diversity model. We then propose a constant approximation algorithm for the problem. We finally evaluate the effectiveness of the proposed algorithm by extensive experimental simulations, using different real datasets. Experimental results show that the users identified from a social network by the proposed algorithm have much larger influence than that found by existing algorithms.

You followed my bot! Transforming robots into influential users in Twitter

Systems that classify influential users in social networks have been used frequently and are referenced in scientific papers and in the media as an ideal standard of evaluation of influence in the Twitter social network. We consider such systems of evaluation to be complex and subjective, and we therefore suspect that they are vulnerable and easy to manipulate. Based on this, we performed experiments and analysis of two systems for ranking influence: Klout and Twitalyzer. We created simple robots capable of interacting by means of Twitter accounts, and we measured how influent they were. Our results show that it is possible to become influential through simple strategies. This suggests that the systems do not have ideal means to measure and classify influence.