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 MDL-based Model of Gender Knowledge Acquisition

This paper presents an iterative model of\ud knowledge acquisition of gender information\ud associated with word endings in\ud French. Gender knowledge is represented\ud as a set of rules containing exceptions.\ud Our model takes noun-gender pairs as input\ud and constantly maintains a list of\ud rules and exceptions which is both coherent\ud with the input data and minimal with\ud respect to a minimum description length\ud criterion. This model was compared to\ud human data at various ages and showed a\ud good fit. We also compared the kind of\ud rules discovered by the model with rules\ud usually extracted by linguists and found\ud interesting discrepancies

Computational Cognitive Models of Summarization Assessment Skills

This paper presents a general computational cognitive model of the way a summary is assessed by teachers. It is based on models of two subprocesses: determining the importance of sentences and guessing the cognitive rules that the student may have used. All models are based on Latent Semantic Analysis, a computational model of the representation of the meaning of words and sentences. Models' performances are compared with data from an experiment conducted with 278 middle school students. The general model was implemented in a learning environment designed for helping students to write summaries

Electric potential variations associated with yearly lake level variations

Electric potential variations have been recorded from November 1995 to February 1996 and continuously since October 1996 at 14 measurement points on a one km wide ridge separating two lakes in the French Alps. The levels of the lakes vary by several tens of meters on a yearly cycle, inducing stress variations and fluid percolation. At one point, unambiguous variations as large as 120 mV are observed over a year, linearly correlated with the levels of the lakes with a magnitude of 2 mV per meter of water level change. This particular measurement point lies at the edge of a SP anomaly, which supports the presence of a localized zone of ground water flow forced by the lake level, suggesting an electrokinetic mechanism. The observed correlation implies a ζ‐potential of the order of ‐8 mV for a 60 Ωm electrolyte, in agreement with laboratory measurements

Reconfiguration of Distributed Information Fusion System ? A case study

Information Fusion Systems are now widely used in different fusion contexts, like scientific processing, sensor networks, video and image processing. One of the current trends in this area is to cope with distributed systems. In this context, we have defined and implemented a Dynamic Distributed Information Fusion System runtime model. It allows us to cope with dynamic execution supports while trying to maintain the functionalities of a given Dynamic Distributed Information Fusion System. The paper presents our system, the reconfiguration problems we are faced with and our solutions.Comment: 6 pages - Preprint versio

Variational solution of the Gross-Neveu model at finite temperature in the large N limit

We use a nonperturbative variational method to investigate the phase transition of the Gross-Neveu model. It is shown that the variational procedure can be generalized to the finite temperature case. The large N result for the phase transition is correctly reproduced.Comment: 12 p., 1 fig, this is the version which will appear in the Phys Lett B, it differs from the previous one in what concerns the introduction and conclusions (re written), several references have been adde

Data Assimilation for hyperbolic conservation laws. A Luenberger observer approach based on a kinetic description

Developing robust data assimilation methods for hyperbolic conservation laws is a challenging subject. Those PDEs indeed show no dissipation effects and the input of additional information in the model equations may introduce errors that propagate and create shocks. We propose a new approach based on the kinetic description of the conservation law. A kinetic equation is a first order partial differential equation in which the advection velocity is a free variable. In certain cases, it is possible to prove that the nonlinear conservation law is equivalent to a linear kinetic equation. Hence, data assimilation is carried out at the kinetic level, using a Luenberger observer also known as the nudging strategy in data assimilation. Assimilation then resumes to the handling of a BGK type equation. The advantage of this framework is that we deal with a single "linear" equation instead of a nonlinear system and it is easy to recover the macroscopic variables. The study is divided into several steps and essentially based on functional analysis techniques. First we prove the convergence of the model towards the data in case of complete observations in space and time. Second, we analyze the case of partial and noisy observations. To conclude, we validate our method with numerical results on Burgers equation and emphasize the advantages of this method with the more complex Saint-Venant system

The Web Browser as a Tool: A Programmatic Approach to Graphic Design on the Web

In recent years, the web browsers rendering capabilities have grown considerably. However, it remains a window through which design is seen rather than being used as a tool. This thesis seeks to develop a programmatic method that questions the web browsers original role as a display and redefines it by investigating its alternative role as a tool in the graphic design process. Through exploratory work, this research demonstrates that the web browser can be a fertile space for visual experimentation. This thesis demonstrates that graphic designers can benefit from a more pragmatic and logical approach to creation and invites them to adopt a process similar to a programmers process using the web browser as a tool