Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure

STRATEGI KOMUNIKASI PEMASARAN PARIWISATA DALAM MEMPERKENALKAN WISATA DAERAH (Studi pada Dinas Kebudayaan Pemuda Olahraga dan Pariwisata Kab. Sumenep)

Current technological developments make it easier to promote on social media. Factors such as the use of social media, collaboration with local influencers, and unique experiences are important in designing interesting and relevant strategies. This research uses a naturalism paradigm and interview and documentation methods. This research aims to find out the strategies used by the District Youth, Sports and Tourism Culture Service. Sumenep in introducing regional tourism. This research also uses a descriptive qualitative approach. The results of the research show that the Sumenep Regency Culture, Youth, Sports and Tourism Office has implemented a pull, push and pass strategy. However, according to researchers, DISBUDPORAPAR Kab. Sumenep still requires evaluation of the website and realization of activities in collaboration with Instagram, TikTok and YouTube influencers

Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines

Creating the Technopolis: High-Technology Development in Austin, Texas

New institutional alliances, driven by the rapid increase in and diversity of new technologies, are altering the strategy and tactics of economic development. As a result, communities across the world are seeking to create modern technopoleis or city-states that interactively link technology commercialization with public and private sectors to spur economic growth and diversification through high-technology company development. This paper develops the conceptual framework of a technopolis wheel from studying the dynamics of high-technology development and economic growth in Austin, Texas. It describes seven segments within the technopolis: the university, large technology companies, small technology companies, federal government, state government, local government and support groups. (Author's preprint.)IC2 Institut

Identifying influencers in a social network : the value of real referral data

Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers: (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers