New time-based model to identify the influential users in online social networks

Abstract

Purpose Users are the key players in an online social network (OSN), so the behavior of the OSN is strongly related to their behavior. User weight refers to the influence of the users on the OSN. The purpose of this paper is to propose a method to identify the user weight based on a new metric for defining the time intervals. Design/methodology/approach The behavior of an OSN changes over time, thus the user weight in the OSN is different in each time frame. Therefore, a good metric for estimating the user weight in an OSN depends on the accuracy of the metric used to define the time interval. New metric for defining the time intervals is based on the standard deviation and identifies that the user weight is based on a simple exponential smoothing model. Findings The results show that the proposed method covers the maximum behavioral changes of the OSN and is able to identify the influential users in the OSN more accurately than existing methods. Research limitations/implications In event detection, when a terrorist attack occurs as an event, knowing the influential users help us to know the leader of the attack. Knowing the influential user in each time interval based on this study can help us to detect communities which formed around these people. Finally, in marketing, this issue helps us to have a targeted advertising. Practical implications User effect is a significant issue in many OSN domain problems, such as community detection, event detection and recommender systems. Originality/value Previous studies do not give priority to the recent time intervals in identifying the relative importance of users. Thus, defining a metric to compute a time interval that covers the maximum changes in the network is a major shortcoming of earlier studies. Some experiments were conducted on six different data sets to test the performance of the proposed model in terms of the computed time intervals and user weights.

Taxonomy of Link Prediction for Social Network Analysis: A Review

“…Nodes are the key features in OSN, and the behavior of nodes is strongly related to the behavior of OSN [115]. J. Wu et al [72] proposed a novel framework called Influential Node Identification Link Prediction (INILP) to integrate the identification technique for the famous and influential node.…”

A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks

“…They defined leadership as the node with the highest degree centrality. However, Mahmoudi, et al, [49] showed that considering the node with the highest degree centrality without considering the weight of each user in each time interval cannot determine who among the nodes (users) is more influential. Nevertheless, the idea presented by Haji Seyed Javadi and colleagues is significant as they proved that the most important users can attract other users.…”

“…Here, the term user weight refers to the influence of a user in an OSN. In recent years, many researchers have sought to identify the influential users and user weight [49] [76]. This is because "in an OSN, each user has a specific weight, which refers to the influence of the user on the OSN, and the weight of each user is different.…”

“…A user's weight is a key indicator of the user's influence on the OSN; where the weight of the user is greater, the more influence that the user has on the OSN as compared to other users. An accurate understanding of the role of users is fundamental to the solving of many online social network (OSN) domain problems, such as community detection, event detection, and marketing" [49]. We use the method presented in [49] and deploys the simple exponential smoothing (5) is a method to estimate the current value based on previous values in a time series with a coefficient α.…”

“…An accurate understanding of the role of users is fundamental to the solving of many online social network (OSN) domain problems, such as community detection, event detection, and marketing" [49]. We use the method presented in [49] and deploys the simple exponential smoothing (5) is a method to estimate the current value based on previous values in a time series with a coefficient α. This method is widely used in time series data mining and is expressed as:…”