Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks

Abstract

Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network, thus benefiting diverse applications including viral marketing, disease control and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and hence act as precursors for detection of influential nodes 1. The set of influential nodes identified by SmartInf have the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter show the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population.

Information Diffusion Model in Twitter: A Systematic Literature Review

“…This subsection tries to answer the fourth research question (RQ4): What measures do researchers use to rank influencers? For articles with a focus on the study of influential users on Twitter, the measures used to rank influential users are traditional measures such as closeness, betweenness, and PageRank [54][55][56][57]61,63], analytic hierarchy process [20], and buzz rank [62]. The types of measure used to assess influential users can be seen in Table 5.…”

DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

“…On the other hand, user actions on online platforms also provide rich information for distinguishing OUs. Such information includes various respects of a user's online lifestyle, e.g., information cascades [19], [76], [85], user interest [86], and user interactions [77], [87]. Deep learning methods are effective in extracting latent key information, e.g., text and time series.…”

“…OUs have long been shown to be one of the fundamental building blocks of many business problems and social applications, e.g., recommender systems [13], [14], viral marketing [15], and information diffusion [16], [17]. Thus, there are various methods of OU detection in recent literature [13], [18], [19], [20], [21]. However, existing methods suffer from one or more of the three following drawbacks: 1) The network structure sometimes is fragmentary due to users' privacy configurations.…”

Socialization on Sustainable Networks: The Case of eBay Green’s Facebook

“…Conventional social theories have often considered individual actors as independent choice makers who behave without thinking of others, a perspective which disregards the actor's place within the social context [45] (Bhowmick, Gueuning, Delvenne, Lambiotte, & Mitra, 2019). However, SNA not only prioritizes the relationships among actors within a social environment, but also emphasizes individual attributes in order to understand social events [46] (Eleni, Milaiou, Karyotis, & Papavassiliou, 2018).…”