Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model

Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others. Existing works have three key limitations. (1) They do not account for economic considerations of a user in buying/adopting items. (2) Most studies on multiple items focus on competition, with complementary items receiving limited attention. (3) For the network owner, maximizing social welfare is important to ensure customer loyalty, which is not addressed in prior work in the IM literature. In this paper, we address all three limitations and propose a novel model called UIC that combines utility-driven item adoption with influence propagation over networks. Focusing on the mutually complementary setting, we formulate the problem of social welfare maximization in this novel setting. We show that while the objective function is neither submodular nor supermodular, surprisingly a simple greedy allocation algorithm achieves a factor of $(1-1/e-\epsilon)$ of the optimum expected social welfare. We develop \textsf{bundleGRD}, a scalable version of this approximation algorithm, and demonstrate, with comprehensive experiments on real and synthetic datasets, that it significantly outperforms all baselines.Comment: 33 page

Sustainability in FinTechs: An Explanation through Business Model Scalability and Market Valuation

Framework: Financial Technology (FinTech) is an industry composed of diversified firms that combine financial services with innovative technologies. The research question and main goal are attempting to answer whether they are more similar to traditional banks or trendy technological firms deploying their innovativeness to favor financial inclusion and sustainability. Justification: Evaluators may wonder if FinTechs follow the typical evaluation patterns of bank/financial intermediaries or those of technological firms. Preliminary empirical evidence shows that the latter interpretation is the one consistent with the stock-market mood. Objective: This study goes beyond the extant literature, analyzing the differences between FinTechs and traditional banks in market valuation, and showing the potential for digital interaction and cross-pollination of complementary business models. Methodology: The differences will be empirically analyzed with the stock market valuation and the multipliers associated with these firms. Results: The main contribution of this paper is that the appraisal approaches of FinTechs follow those of technological startups, having a revenue model much more scalable than that of a typical bank. FinTechs may so provide a solution for sustainable finance with microfinance and crowdfunding among others. FinTechs and traditional banks may eventually converge towards a common market exploiting co-opetition strategies

Justicia: A Stochastic SAT Approach to Formally Verify Fairness

As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally verify the fairness metrics satisfied by different algorithms on different datasets. In this paper, we propose a \textit{stochastic satisfiability} (SSAT) framework, Justicia, that formally verifies different fairness measures of supervised learning algorithms with respect to the underlying data distribution. We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds. Justicia is scalable, accurate, and operates on non-Boolean and compound sensitive attributes unlike existing distribution-based verifiers, such as FairSquare and VeriFair. Being distribution-based by design, Justicia is more robust than the verifiers, such as AIF360, that operate on specific test samples. We also theoretically bound the finite-sample error of the verified fairness measure.Comment: 24 pages, 7 figures, 5 theorem

Modeling Variability in the Video Domain: Language and Experience Report

This paper reports about a new domain-specific variability modeling language, called VM, resulting from the close collaboration with industrial partners in the video domain. We expose the requirements and advanced variability constructs required to characterize and realize variations of physical properties of a video (such as objects' speed or scene illumination). The results of our experiments and industrial experience show that VM is effective to model complex variability information and can be exploited to synthesize video variants. We concluded that basic variability mechanisms are useful but not enough, attributes and multi-features are of prior importance, and meta-information is relevant for efficient variability analysis. In addition, we questioned the existence of one-size-fits-all variability modeling solution applicable in any industry. Yet, some common needs for modeling variability are becoming apparent such as support for attributes and multi-features.Ce document décrit un nouveau langage de modélisation dédiée à la variabilité, appelé VM, résultant de la collaboration avec des partenaires industriels dans le domaine de la vidéo. Nous exposons les exigences et les constructions de la variabilité avancées requises pour caractériser et implémenter les variations des propriétés physiques d'une vidéo (tels que la vitesse des objets ou l'illumination de la scène). Les résultats de nos expérimentations et de l'expérience industrielle montrent que VM est efficace pour modéliser l'information de variabilité complexe et peut être exploitée pour synthétiser des variantes de vidéo. Nous avons conclu que les mécanismes basiques de la variabilité sont certes utiles, mais insuffisants. Les attributs et multi-caractéristiques sont nécessaires alors que les méta-informations sont pertinentes pour une analyse efficace de la variabilité. En s'appuyant sur notre expérience, nous mettons en doute l'existence d'une solution de modélisation de la variabilité applicable à n'importe quelle industrie et domaine. Néanmoins, certains besoins communs pour la modélisation de la variabilité à sont apparents, comme le support pour les attributs et multi-caractéristiques