Mining of Influencers in Signed Social Network: A Memetic Approach

Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13, 2013

The goal of viral marketing is that, by the virtue of mouth to mouth word spread, a small set of influential customers can influence more customers. Influence maximization (IM) task is used to discover such influential nodes (or customers) from a social network. Existing algorithms for IM adopt Greedy and Lazy forward optimization approaches which assume only positive influence among users and availability of influence probability, the probability that a user is influenced by another. In this work, we propose the T-GT model, which considers both positive (trust) and negative (distrust) influences in social trust networks. We first compute positive and negative influences by mining frequent patterns of actions performed by users. Then, a local search based algorithm called mineSeedLS for node add, exchange and delete operations, is proposed to discover influential nodes from trust networks. Experimental results shows that our approach outperforms Greedy based approach by about 35%.

Ijca Proceedings on Amrita International Conference of Women in Computing 2013, 2013

The World Wide Web is one of the most inevitable notions in the life of mankind. In the most recent times of the world it is much more in advance gaining popularity due to its enormous amount of capability in making the life more impact-able. Online social networks are one of the areas of the World Wide Web where people congregate to share and be part of the various virtual communities. Online social networks are more fascinating to many of us now as they look out for similarly inclined people in order to share in reciprocity their findings, ideals, thoughts, opinions and views. Much like the physical human networks, cybernetics too has people who can influence or influenced by the other. Some are leaders who inevitably influence voluminous people, while others look out to get influenced or to induce inspiration from their leader. Identifying people who exercise maximum influence could be useful in targeting them for marketing, knowledge dissemination and other such purposes. In this paper, we present empirical analysis of levels of influence in the online social network. Findings of this paper may be of great help to elucidate in ascertaining leaders of a social network which in turn can be used to sight the leaders in real world. The leaders of a social network are traced by using Pareto front function. We believe that this is the first study to use Pareto front function to identify the leaders in online social networks. The empirical results prove that the number of leaders in each subsequent level monotonically increase while the number of their followers decreases.

Detection of influential nodes in networks is a key problem of prime importance for a plenty of important practical applications. Social networks, covert networks like terrorist networks have an important feature of " influential " nodes which have great implications ranging from marketing, advertisement, recommendation, controlling the spread of information in a network etc. This survey paper discusses the research on social influence analysis and the various techniques used in the detection of the influential nodes in social networks, terrorist networks et cetera.

Influential users who diffuse information and their followers have interest to this information finally they can maximize diffusion in social networks. Influential users have different influence in diversity domain specificity for instance user may have strong influence in a special topic and another topics have weak influence. So a proposed method presented for identifying influential users based on domain specificity in this paper. This method identified influential users based on domain specificity that features of user's profile and user's actions (e.g. retweet) that influence on diffusion determined by " multiple regression " and user's contents categorized based on keywords by " TF-IDF " and finally influential users identified by " Tree Regression " based on domain specificity in this paper. The detail of this method discussed the fallowing of paper. In order to evaluate the proposed method on Twitter offer application program interface. 420 users selected randomly, they fallow their friends, join to different groups, and generated diversity tweets on Twitter. The main feature, which distinguishes this method from the previously reported methods, is in two key respective. First previous studies have quantified influence in terms of network metrics for instance number of retweet or page rank, our proposed method measured influence in terms of the size Tree Regression. Second the focuses of previous studies were based on the structural of diffusion and feature of content but Influential users have different influence in diversity domain specificity so in our proposed method focused on this feature. Results showed that accuracy of proposed method is 0.69.

J. Intell. Fuzzy Syst., 2021

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, men...

Elsevier, 2023

With the increasing development of social networks, they have turned into important research platforms. Influence maximization is one of the most important research issues in the field of social networks. This problem detects influential k-node with the greatest influence spread. The influence maximization faces two important challenges, time efficiency and optimal selection of seed nodes. In order to solve such challenges, we propose an algorithm based on optimal pruning and scoring adjustment, which is called IMBC for short. The IMBC (Influence Maximization Based on Community structure) algorithm uses optimal pruning and a minimum of dominating nodes to improve time efficiency. In addition, for optimal selection of seed nodes, the IMBC algorithm modulates the scores of nodes with a high Rich-Club coefficient. In order to select influential nodes, we first select an optimal set using the minimum dominating nodes and node scores, with the aim of optimal pruning in influence spread calculations. Because large-scale social networks have many nodes, optimal pruning reduces computational overhead. Then, the seed nodes are selected based on the scoring adjustment. Scoring adjustment is done to avoid the Rich Club phenomenon because avoiding this phenomenon causes a large amount of diffusion in social networks. The experimental results show that the proposed algorithm performs better than the algorithms presented in recent years in influence spread and runtime. Therefore, the IMBC algorithm is a balance between quality and efficiency. Also, in the PGP dataset results, the PHG algorithm with as much as a 5.08% increase in influence spread, and the runtime has decreased by 97%.

In Integrated Intelligent Computing, Communication and Security, 49-57, 2019

The exponential growth in signed social networks in recent years has garnered the interest of numerous researchers in the field. Social balance theory and status theory are the two most prevalent theories of signed social networks and are used for the same purpose. Many researchers have incorporated the concept of social balance theory into their work with community detection problems in order to gain a better understanding of these networks. Social balance theory is suitable for undirected signed social networks; however, it does not consider the direction of the ties formed among users. When dealing with directed signed social networks, researchers simply ignore the direction of ties, which diminishes the significance of the tie direction information. To overcome this, in this chapter we present a mathematical formulation for computing the social status of nodes based on status theory, termed the status factor, which is well suited for directed signed social networks. The status factor is used to quantify social status for each node of overlapping communities in a directed signed social network, and the feasibility of the proposed algorithm for this metric is well illustrated through an example.

2014

In the recent past and in the envisaged future of global business scenario, social networking platform such as : Face book, Twitter, Google plus +, LinkedIn, Orkut etc are visioned to get information, opinions, likes and dislikes, profile matching etc. These inputs are very essential parameters in order to design, devise and deliver many of the marketing and CRM strategies for corporates and organizations. In computational theory a social network can be represented in terms of defined structure either by tree or a graph. The network of nodes could be either static in nature or dynamic in nature as it evolves over period of time by adding or deleting nodes and edges. The study of influential members in a social network is an important research question in social network analysis. In order to find the most influential person, the most central node has to be identified. Centrality is the measure of most influential node, which is measured in terms of centrality metrics. There have been...

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014

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. Network applications can benefit from accompanying these structural differences in propagation schemes. In this paper, we study the optimal influence propagation policies for networks with positive and negative relationship types. We tackle the problem of minimizing the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as frequency of interaction, the strength of friendship and foe ties, propagation delay or the impact factor of the propagating idea. We extend this problem by incorporating the impact of message deterioration and ignorance. We demonstrate our results in both a controlled environment and the Epinions dataset. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of influence propagation compared to naïve myopic algorithms. Index Terms-Socially aware physical systems, network propagation for social media, signed networks, recommender systems.

2019 Amity International Conference on Artificial Intelligence (AICAI), 2019

Influential node detection in social networks has become a vital approach to defining some key players in a network. Many approaches have developed applications of such social network analyses for viral marketing, law enforcement, and collaborative support systems for communities using clustering algorithms or centrality measures. One of the most efficient ways to identify influential nodes in a network is to find centralities of the nodes based on their information gain, which takes into account the information gains of their neighbouring nodes as well. In this paper, we propose a hybrid model of influential node search based on such centralities like the degree centrality, betweenness centrality and information gain of the nodes to provide a more precise measure of influence in any network. Once we obtain priority nodes from different centrality measures including EVC, we apply quantitative efficiency of communication to obtain better influential relationships between the nodes in the dataset.