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

A Data-Based Approach to Social Influence Maximization

Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. Most of the literature on this topic has focused exclusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, we study influence maximization from a novel data-based perspective. In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread. Our approach also learns the different levels of influenceability of users, and it is time-aware in the sense that it takes the temporal nature of influence into account. We show that influence maximization under the credit distribution model is NP-hard and that the function that defines expected spread under our model is submodular. Based on these, we develop an approximation algorithm for solving the influence maximization problem that at once enjoys high accuracy compared to the standard approach, while being several orders of magnitude faster and more scalable.Comment: VLDB201

Election Manipulation in Social Networks with Single-Peaked Agents

Several elections run in the last years have been characterized by attempts to manipulate the result of the election through the diffusion of fake or malicious news over social networks. This problem has been recognized as a critical issue for the robustness of our democracy. Analyzing and understanding how such manipulations may occur is crucial to the design of effective countermeasures to these practices. Many studies have observed that, in general, to design an optimal manipulation is usually a computationally hard task. Nevertheless, literature on bribery in voting and election manipulation has frequently observed that most hardness results melt down when one focuses on the setting of (nearly) single-peaked agents, i.e., when each voter has a preferred candidate (usually, the one closer to her own belief) and preferences of remaining candidates are inversely proportional to the distance between the candidate position and the voter's belief. Unfortunately, no such analysis has been done for election manipulations run in social networks. In this work, we try to close this gap: specifically, we consider a setting for election manipulation that naturally raises (nearly) single-peaked preferences, and we evaluate the complexity of election manipulation problem in this setting: while most of the hardness and approximation results still hold, we will show that single-peaked preferences allow to design simple, efficient and effective heuristics for election manipulation

Modelling approaches to food waste : discrete event simulation; machine learning; Bayesian networks; agent-based modelling; and mass balance estimation

The generation of food waste at both the supplier and the consumer levels stems from a complex set of interacting behaviours. Computational and mathematical models provide various methods to simulate, diagnose and predict different aspects within the complex system of food waste generation and prevention. This chapter outlines four different modelling approaches that have been used previously to investigate food waste: discrete event simulation, which has been used to examine how the shelf life of milk and many actions taken around shopping and use of milk within a household influence food waste; machine learning and Bayesian networks, which have been used to provide insight into the determinants of household food waste; agent-based modelling, which has been used to provide insight into how innovation can reduce retail food waste; and mass balance estimation, which has been used to model and estimate food waste from data related to human metabolism and calories consumed

Can Few Lines of Code Change Society ? Beyond fack-checking and moderation : how recommender systems toxifies social networking sites

As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed at large scale by digital platforms such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive biais on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric and to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to an overexposure of users to negative content (up to 300\% for some of them), a phenomenon called algorithmic negativity bias, to a polarization of the opinion landscape, and to a concentration of social power in the hands of the most toxic users. The latter are more than twice as numerous in the top 1\% of the most influential users than in the overall population. Overall, our findings highlight the urgency to identify harmful implementations of recommender systems to individuals and society in order better regulate their deployment on systemic SNSs

Influence Maximization in Large Scale Graphs

Maximizing influence in graphs, typically applied to Social Networks, is the problem of finding a set of nodes with the highest overall influence on the entire graph. In marketing domain for example, it is used to find the set of people who have the highest influence on their local communities. As a result, instead of blindly marketing a product to a large group of people, the product is marketed to this group of selected users, and they will in turn help spreading the word. The problem has been studied extensively, and several state of the art methods have been proposed. But all of these methods have one common flaw, none of them are scalable. Even on small graphs, current methods take extremely long amount of time and introduction of bigger data sets have rendered some of these methods completely useless. Over the past two decades, collection of data has become easier and a very common practice. This is mostly credited to the advancements in hardware and software technologies as well as the introduction of World Wide Web. To overcome issues related to big data sets, large scale data processing platforms have been developed to tackle scalability issues of problems similar to the influence maximization. Most notably are the two frameworks called Hadoop and Spark that contain many features for simple data processing, machine learning and graph processing. In this thesis work, some of the current influence maximization algorithms are implemented in these two frameworks, some new methods are proposed, experiments on graphs of different sizes are performed and the results are reported