Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes

Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily, and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities

Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization

We study the r-complex contagion influence maximization problem. In the influence maximization problem, one chooses a fixed number of initial seeds in a social network to maximize the spread of their influence. In the r-complex contagion model, each uninfected vertex in the network becomes infected if it has at least r infected neighbors. In this paper, we focus on a random graph model named the stochastic hierarchical blockmodel, which is a special case of the well-studied stochastic blockmodel. When the graph is not exceptionally sparse, in particular, when each edge appears with probability omega (n^{-(1+1/r)}), under certain mild assumptions, we prove that the optimal seeding strategy is to put all the seeds in a single community. This matches the intuition that in a nonsubmodular cascade model placing seeds near each other creates synergy. However, it sharply contrasts with the intuition for submodular cascade models (e.g., the independent cascade model and the linear threshold model) in which nearby seeds tend to erode each others\u27 effects. Finally, we show that this observation yields a polynomial time dynamic programming algorithm which outputs optimal seeds if each edge appears with a probability either in omega (n^{-(1+1/r)}) or in o (n^{-2})

Efficient Truss Maintenance in Evolving Networks

Truss was proposed to study social network data represented by graphs. A k-truss of a graph is a cohesive subgraph, in which each edge is contained in at least k-2 triangles within the subgraph. While truss has been demonstrated as superior to model the close relationship in social networks and efficient algorithms for finding trusses have been extensively studied, very little attention has been paid to truss maintenance. However, most social networks are evolving networks. It may be infeasible to recompute trusses from scratch from time to time in order to find the up-to-date $k$-trusses in the evolving networks. In this paper, we discuss how to maintain trusses in a graph with dynamic updates. We first discuss a set of properties on maintaining trusses, then propose algorithms on maintaining trusses on edge deletions and insertions, finally, we discuss truss index maintenance. We test the proposed techniques on real datasets. The experiment results show the promise of our work

Study the factors affecting the choice of higher education nonprofit institutions from candidate’s view (Case study: Preuniversity students in Qaemshahr)

This study was conducted to investigate the factors affecting the choice of higher education nonprofit institutions by pre-university students in Qaemshahr.From the population of the study 3176 people is randomly selected and a questionnaire was used to collect data that its reliability is 91/0 and its validity was approved by using the same external research and opinions of experts and university professors, finally, data from 320 students were analyzed by using SPSS software. Toanalyze the data t-test and Friedman test was used. The results show that among the studied variables,  including economic factors, factors related to the university, personal factors and social factors, economic factors are most important to students and social factors are the least important.Key words: economic factors, factors related to the university, personal factors, social  factors, higher education non-profit institution

Predicting mobile advertising response using consumer colocation networks

Building on results from economics and consumer behavior, the authors theorize that consumers' movement patterns are informative of their product preferences, and this study proposes that marketers monetize this information using dynamic networks that capture colocation events (when consumers appear at the same place at approximately the same time). To support this theory, the authors study mobile advertising response in a panel of 217 subscribers. The data set spans three months during which participants were sent mobile coupons from retailers in various product categories through a smartphone application. The data contain coupon conversions, demographic and psychographic information, and information on the hourly GPS location of participants and on their social ties in the form of referrals. The authors find a significant positive relationship between colocated consumers' response to coupons in the same product category. In addition, they show that incorporating consumers' location information can increase the accuracy of predicting the most likely conversions by 19%. These findings have important practical implications for marketers engaging in the fast-growing location-based mobile advertising industry

PLAYING THE GAME: VIDEO GAMES AND VIDEO GAME STREAMING PLATFORMS AS MARKETING COMMUNICATION CHANNELS

While video games are generally viewed as a form of entertainment for a small subset of people, in reality they provide a channel for nearly 3.2 billion people to interact with others and offer multiple pathways for marketers to interact with consumers. Pair this alongside 140 million unique consumers who consumer nearly 24 billion hours of content on video game streaming platforms (VGSPs), such as Twitch, and there is a deep need for marketers to understand how to engage consumers in these environments. This dissertation provides a conceptualization of the video game ecosystem as well as the types of influencers on VGSPs, while highlighting important marketer-to-consumer interactions that occur through these platforms. In the first essay, I provide a new framework called the video game ecosystem to show how video games can be leveraged as a marketing communication channel and how it differs from other popular channels, such as social media and television. Furthermore, I identify 7 testable propositions from the marketer’s perspective I believe will meaningfully direct the current marketing practice while shaping marketing research’s future including outlining the ways marketers should build and present content through this channel, highlighting marketer-consumer interactions unique to this ecosystem, and showcasing the potential ways firms can leverage the video game ecosystem in their marketing strategies. Finally, in this essay I present 12 future research areas to help kickstart marketing research in this domain. In the second essay, I present a new conceptualization of influencer marketing through VGSPs. Specifically, I highlight how influencer-to-influencer (I2I), influencer-to-consumer (I2C), and consumer-to-consumer (C2C) relationships differ on VGSPs compared to traditional social media platforms, and how these relationships impact consumers downstream. I identify two unique types of influencers on VGSPs (video game streamers and esports athletes) and provide 6 novel propositions regarding the formation of social networks around these influencers. Finally, I provide 8 research areas to help shape the future of consumer research across multiple domains

Predicting voting outcomes in presence of communities

We study several fairness notions in allocating indivisible chores (i.e., items with non-positive values): envy-freeness and its relaxations. For allocations under each fairness criterion, we establish their approximation guarantees for other fairness criteria. Under the setting of additive cost functions, our results show strong connections between these fairness criteria and, at the same time, reveal intrinsic differences between goods allocation and chores allocation. Furthermore, we investigate the efficiency loss under these fairness constraints and establish their prices of fairness

The effects of local and global link creation mechanisms on contagion processes unfolding on time-varying networks

Social closeness and popularity are key ingredients that shape the emergence and evolution of social connections over time. Social closeness captures local reinforcement mechanisms which are behind the formation of strong ties and communities. Popularity, on the other hand, describes global link formation dynamics which drive, among other things, hubs, weak ties and bridges between groups. In this chapter, we characterize how these mechanisms affect spreading processes taking place on time-varying networks. We study contagion phenomena unfolding on a family of artificial temporal networks. In particular, we revise four different variations of activity-driven networks that capture i) heterogeneity of activation patterns ii) popularity iii) the emergence of strong and weak ties iv) community structure. By means of analytical and numerical analyses we uncover a rich and process dependent phenomenology where the interplay between spreading phenomena and link formation mechanisms might either speed up or slow down the spreadin

Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks

The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas. In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments