Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use.

Use of uniform designs in combination with neural networks for viral infection process development

This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions.

A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) lambda-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The lambda-coverage problem is concerned with finding a set of k key nodes having minimal size that can influence a given percentage lambda of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the lambda-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. Note to Practitioners-In recent times, social networks have received a high level of attention due to their proven ability in improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market using viral marketing techniques, etc. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. This problem is called the target set selection problem and has two variants: 1) the top-k nodes problem, where we are required to identify a set of k influential nodes that maximize the number of nodes being influenced in the network and 2) the lambda-coverage problem which involves finding a set of influential nodes having minimum size that can influence a given percentage lambda of the nodes in the entire network. There are many existing algorithms in the literature for solving these problems. In this paper, we propose a new algorithm which is based on a novel interpretation of information diffusion in a social network as a cooperative game. Using this analogy, we develop an algorithm based on the Shapley value of the underlying cooperative game. The proposed algorithm outperforms the existing algorithms in terms of generality or computational complexity or both. Our results are validated through extensive experimentation on both synthetically generated and real-world data sets.

Determining Top K Nodes in Social Networks using the Shapley Value

In this paper, we consider the problem of selecting, for any given positive integer k, the top-k nodes in a social network, based on a certain measure appropriate for the social network. This problem is relevant in many settings such as analysis of co-authorship networks, diffusion of information, viral marketing, etc. However, in most situations, this problem turns out to be NP-hard. The existing approaches for solving this problem are based on approximation algorithms and assume that the objective function is sub-modular. In this paper, we propose a novel and intuitive algorithm based on the Shapley value, for efficiently computing an approximate solution to this problem. Our proposed algorithm does not use the sub-modularity of the underlying objective function and hence it is a general approach. We demonstrate the efficacy of the algorithm using a co-authorship data set from e-print arXiv (www.arxiv.org), having 8361 authors.

Optimal incentive timing strategies for product marketing on social networks

We consider the problem of devising incentive strategies for viral marketing of a product. In particular, we assume that the seller can influence penetration of the product by offering two incentive programs: a) direct incentives to potential buyers (influence) and b) referral rewards for customers who influence potential buyers to make the purchase (exploit connections). The problem is to determine the optimal timing of these programs over a finite time horizon. In contrast to algorithmic perspective popular in the literature, we take a mean-field approach and formulate the problem as a continuous-time deterministic optimal control problem. We show that the optimal strategy for the seller has a simple structure and can take both forms, namely, influence-and-exploit and exploit-and-influence. We also show that in some cases it may optimal for the seller to deploy incentive programs mostly for low degree nodes. We support our theoretical results through numerical studies and provide practical insights by analyzing various scenarios.

La aplicación empresarial del marketing viral y el efecto boca-oreja electrónico. Opiniones de las empresas

[ES] La práctica tradicional del boca-oreja, natural desde hace años como modo de publicidad efectiva sin recurrir a grandes inversiones ni contrataciones en medios masivos se ha convertido en una nueva posibilidad de marketing con la llegada de las nuevas tecnologías e Internet. Así ha aparecido una nueva estrategia de marketing que consiste en explotar las redes sociales preexistentes para producir incrementos exponenciales en conocimiento de marca, denominada marketing viral. El efecto "boca-oreja online" que genera es una herramienta poderosa para las empresas, aunque su verdadero potencial está aún por descubrir. El objetivo del presente trabajo de investigación es analizar este fenómeno a través de una amplia revisión bibliográfica del término, así como un estudio empírico consistente en una entrevista en profundidad realizada a una muestra de importantes empresas españolas. Los resultados indican que el marketing viral puede ser empleado para el beneficio, tanto de grandes empresas con grandes presupuestos, como de pequeños negocios. Las experiencias exitosas de su utilización demuestran que, cuando se usa integrado en el resto de estrategias comerciales de la empresa, puede mejorar la recomendación de la marca e incrementar su notoriedad en el mercado. El marketing viral puede ser ventajoso a la hora de lanzar un nuevo producto al mercado, sin embargo, la efectividad y la medición de las campañas son vistas por muchos académicos y profesionales como un punto débil de la estrategia.

Adaptive Probabilistic Topic Models for Social Networks

Thesis (Master's)--University of Washington, 2012

HIV-1 drug resistance transmission networks in southwest Switzerland.

To determine viral subtypes and resistance mutations to antiretroviral treatment (ART) in untreated HIV-1 acutely infected subjects from Southwest Switzerland. Clinical samples were obtained from the HIV primary infection cohort from Lausanne. Briefly, pol gene was amplified by nested PCR and sequenced to generate a 1?kb sequence spanning protease and reverse transcriptase key protein regions. Nucleotide sequences were used to assess viral genotype and ART resistance mutations. Blood specimens and medical information were obtained from 30 patients. Main viral subtypes corresponded to clade B, CRF02_AG, and F1. Resistant mutations to PIs consisted of L10V and accessory mutations 16E and 60E present in all F1 clades. The NNRTI major resistant mutation 103N was detected in all F1 viruses and in other 2 clades. Additionally, we identified F1 sequences from other 6 HIV infected and untreated individuals from Southwest Switzerland, harboring nucleotide motifs and resistance mutations to ART as observed in the F1 strains from the cohort. These data reveal a high transmission rate (16.6%) for NNRTI resistant mutation 103N in a cohort of HIV acute infection. Three of the 5 resistant strains were F1 clades closely related to other F1 isolates from HIV-1 infection untreated patients also coming from Southwest Switzerland. Overall, we provide strong evidence towards an HIV-1 resistant transmission network in Southwest Switzerland. These findings have relevant implications for the local molecular mapping of HIV-1 and future ART surveillance studies in the region.

Computer "Insecurity" and Viral Attacks : Liability Issues Regarding Unsafe Computer Systems Under Quebec Law

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"Computer ""Insecurity"" and Viral Attacks:Liability Issues Regarding Unsafe Computer Systems Under Quebec Law"

Dans un contexte où les virus informatiques présentent un risque sérieux pour les réseaux à travers le globe, il est impératif de retenir la responsabilité des compagnies qui n’y maintiennent pas une sécurité adéquate. À ce jour, les tribunaux québécois n’ont pas encore été saisis d’affaires en responsabilité pour des virus informatiques. Cet article brosse un portrait général de la responsabilité entourant les virus informatiques en fonction des principes généraux de responsabilité civile en vigueur au Québec. L’auteur propose des solutions pour interpréter les trois critères traditionnels ­ la faute, le dommage et le lien causal ­ en mettant l’accent sur l’obligation de précaution qui repose sur les épaules de l’administrateur de réseau. Ce joueur clé pourrait bénéficier de l’adoption de dispositions générales afin de limiter sa responsabilité. De plus, les manufacturiers et les distributeurs peuvent également partager une partie de la responsabilité en proportion de la gravité de leur faute. Les entreprises ont un devoir légal de s’assurer que leurs systèmes sont sécuritaires afin de protéger les intérêts de leurs clients et des tiers.

Viral marketing and the new competitive advantage

The role played by viral marketing has received considerable academic and digital media attention recently. Key issues in viral marketing have been examined through the lens of the mode of marketing message transmission, including self-replicating on the basis of quality difference, individuals’ emotional needs, as well as how users are connected across various social networks. This paper presents a review and analysis of viral marketing studies from 2001 to the present day. It investigates how viral marketing facilitate the diffusion of social media products and the relationship between marketers and these product users by taking a look at the implementation of viral marketing in two European online game firms Jagex Games Studio and Rovio Entertainment. The results from this review and analysis indicate that viral marketing plays an important role in accelerating the interaction between marketers and users (as well as the user groups) in the field of digital media and high tech consumption. Therefore, it is evident that firms should understand the social contagion process and target well-connected users purposefully in order to create its competitive advantage.

Word of mouth marketing in the digital age: a case study analysis of viral marketing campaigns

A era digital viu a ascensão da empresa focada no consumidor. Todos os dias, marcas e produtos são objeto de milhões de conversas em que os consumidores trocam pontos de vista, opiniões e informações antes de tomar a decisão de compra. Os profissionais de marketing entenderam a importância do boca a boca como um novo canal de comunicação estratégica e começaram a ancorar técnicas de boca a boca à estratégia de marketing global. A propaganda tornou-se um negócio de conversa: o novo desafio do marketing é criar um conteúdo envolvente e compartilhável que possa expandir e ressoar dentro de redes de sociais de consumidores. Quanto mais as pessoas falam sobre a sua marca, mais a probabilidade de ganhar a competição dura e aumentar as vendas. Mas como o boca a boca funciona? O que faz uma campanha de marketing um sucesso viral? Este trabalho tem como objetivo fornecer uma análise abrangente da teoria de marketing de boca a boca e usa um modelo descritivo para investigar as variáveis-chave de campanhas bem-sucedidas de marketing viral, a fim de proporcionar insights e sugestões para as práticas de marketing viral.

Next-generation sequencing reveals large connected networks of intra-host HCV variants

Background: Next-generation sequencing (NGS) allows for sampling numerous viral variants from infected patients. This provides a novel opportunity to represent and study the mutational landscape of Hepatitis C Virus (HCV) within a single host.Results: Intra-host variants of the HCV E1/E2 region were extensively sampled from 58 chronically infected patients. After NGS error correction, the average number of reads and variants obtained from each sample were 3202 and 464, respectively. The distance between each pair of variants was calculated and networks were created for each patient, where each node is a variant and two nodes are connected by a link if the nucleotide distance between them is 1. The work focused on large components having > 5% of all reads, which in average account for 93.7% of all reads found in a patient. The distance between any two variants calculated over the component correlated strongly with nucleotide distances (r = 0.9499; p = 0.0001), a better correlation than the one obtained with Neighbour-Joining trees (r = 0.7624; p = 0.0001). In each patient, components were well separated, with the average distance between (6.53%) being 10 times greater than within each component (0.68%). The ratio of nonsynonymous to synonymous changes was calculated and some patients (6.9%) showed a mixture of networks under strong negative and positive selection. All components were robust to in silico stochastic sampling; even after randomly removing 85% of all reads, the largest connected component in the new subsample still involved 82.4% of remaining nodes. In vitro sampling showed that 93.02% of components present in the original sample were also found in experimental replicas, with 81.6% of reads found in both. When syringe-sharing transmission events were simulated, 91.2% of all simulated transmission events seeded all components present in the source.Conclusions: Most intra-host variants are organized into distinct single-mutation components that are: well separated from each other, represent genetic distances between viral variants, robust to sampling, reproducible and likely seeded during transmission events. Facilitated by NGS, large components offer a novel evolutionary framework for genetic analysis of intra-host viral populations and understanding transmission, immune escape and drug resistance.

Global lymphoid tissue remodeling during a viral infection is orchestrated by a B cell-lymphotoxin-dependent pathway

Adaptive immune responses are characterized by substantial restructuring of secondary lymphoid organs. The molecular and cellular factors responsible for virus-induced lymphoid remodeling are not well known to date. Here we applied optical projection tomography, a mesoscopic imaging technique, for a global analysis of the entire 3-dimensional structure of mouse peripheral lymph nodes (PLNs), focusing on B-cell areas and high endothelial venule (HEV) networks. Structural homeostasis of PLNs was characterized by a strict correlation between total PLN volume, B-cell volume, B-cell follicle number, and HEV length. After infection with lymphocytic choriomeningitis virus, we observed a substantial, lymphotoxin (LT) beta-receptor-dependent reorganization of the PLN microarchitecture, in which an initial B-cell influx was followed by 3-fold increases in PLN volume and HEV network length on day 8 after infection. Adoptive transfer experiments revealed that virus-induced PLN and HEV network remodeling required LTalpha(1)beta(2)-expressing B cells, whereas the inhibition of vascular endothelial growth factor-A signaling pathways had no significant effect on PLN expansion. In summary, lymphocytic choriomeningitis virus-induced PLN growth depends on a vascular endothelial growth factor-A-independent, LT- and B cell-dependent morphogenic pathway, as revealed by an in-depth mesoscopic analysis of the global PLN structure.