In this article, we propose a new measure of firms' dyadic business proximity. Specifically, we analyze the unstructured texts that describe firms' businesses using the natural language processing technique of topic modeling, and develop a novel business proximity measure based on the output. When compared with the existent methods, our approach provides finer granularity on quantifying firms' similarity in the spaces of product, market, and technology. We then show our measure's effectiveness through an empirical analysis using a unique dataset of recent mergers and acquisitions in the U.S. high technology industry. Building upon the literature, our model relates the likelihood of matching of two firms in a merger or acquisition transaction to their business proximity and other characteristics. We particularly employ a class of statistical network analysis methods called exponential random graph models to accommodate the relational nature of the data.

Customer care calls serve as a direct channel for a service provider to learn feedbacks from their customers. They reveal details about the nature and impact of major events and problems observed by customers. By analyzing the customer care calls, a service provider can detect important events to speed up problem resolution. However, automating event detection based on customer care calls poses several significant challenges. First, the relationship between customers' calls and network events is blurred because customers respond to an event in different ways. Second, customer care calls can be labeled inconsistently across agents and across call centers, and a given event naturally give rise to calls spanning a number of categories. Third, many important events cannot be detected by looking at calls in one category. How to aggregate calls from different categories for event detection is important but challenging. Lastly, customer care call records have high dimensions (e.g., thousands of categories in our dataset). In this paper, we propose a systematic method for detecting events in a major cellular network using customer care call data. It consists of three main components: (i) using a regression approach that exploits temporal stability and low-rank properties to automatically learn the relationship between customer calls and major events, (ii) reducing the number of unknowns by clustering call categories and using L1norm minimization to identify important categories, and (iii) employing multiple classifiers to enhance the robustness against noise and different response time. For the detected events, we leverage Twitter social media to summarize them and to locate the impacted regions. We show the effectiveness of our approach using data from a large cellular service provider in the US.

Users around the world have embraced new generation of mobile devices such as the smartphones at a remarkable rate. These devices are equipped with powerful communication and computation capabilities and they enable a wide range of exciting location-based services, e.g., location based ads, content prefetching etc. Many of these services can benefit from a better understanding of the smartphone user mobility, which may differ significantly from the general user mobility. Hence, previous works on understanding user mobility models and predicting user mobility may not directly apply to smartphone users. To overcome this, in this paper we analyze data from two popular location based social networks, where the users are real smartphone users and the places they check-in represent the typical locations where they use their smartphone applications. Specifically, we analyze how individual users move across different locations. We identify several factors that affect user mobility and their relative significance. We then leverage these factors to perform individual mobility prediction. We further show that our mobility prediction yields significant benefit to two important location based applications: content prefetching and shared ride recommendation.

This paper proposes VideoFountain, a novel service that deploys kiosks at popular venues to store and transmit digital media to users' personal devices using Wi-Fi access points, which may not have Internet connectivity. We leverage mobile users to deliver content to these kiosks. A key component in this design is an in-depth understanding of user mobility. We gather real mobility traces from two largest location-based social networks (Foursquare and Gowalla) and analyze both macroscopic and microscopic human mobility in different cities. Based on the insights we gain, we study several algorithms to determine the initial placement of content and design routing algorithms to optimize the content delivery. We further consider several practical issues, such as how to incentivize users to forward content, how to manage copyrights, how to ensure security, and how to achieve service discovery. We demonstrate the feasibility of VideoFountain using trace-driven simulations.