Rockstar Effect in Distributed Project Management on GitHub Social Networks

The internet has become increasingly social, opening up new space for online collaboration and distributed project management. Decentralized management techniques such as open-source software, distributed development, and software-as-a-service allow software developers to easily connect online and to solve complex problems collaboratively. Online rockstars, who are well-respected in a community and are followed by numerous other users, often influence the decisions of project managers and clients in software development. Understanding the effects of these rockstars can greatly facilitate technology development and adoption in distributed project management. This paper presents a study of the GitHub social network to understand rockstar effect in distributed project management. In GitHub, developers often collaborate in distributed teams and interact in their online social networks, which evolve with the popularity of software repositories and actions of rockstars. To understand how rockstars influence the popularity of software repositories, this research constructed temporal social networks from 2015 to 2017 between 13.5 million software repositories and 2.6 million GitHub users and examined the evolvement of the behavior of 245,501 rockstar followers. The results show that the more followers a rockstar has, the more triadic events there are in his/her participated repository. And the difference of a number of events between top rockstar and other rockstars is much higher in participative events than in contributive events, indicating higher triadic influence from top rockstar in those events for technology development in distributed project management

Modeling Multi-Dimensional Datasets via a Fast Scale-Free Network Model

Compared with network datasets, multi-dimensional data are much more common nowadays. If we can model multi-dimensional datasets into networks with accurate network properties, while, in the meantime, preserving the original dataset features, we can not only explore the dataset dynamic but also acquire abundant synthetic network data. This paper proposed a fast scale-free network model for large-scale multi-dimensional data not limited to the network domain. The proposed network model is dynamic and able to generate scale-free graphs within linear time regardless of the scale or field of the modeled dataset. We further argued that in a dynamic network where edge-generation probability represents influence, as the network evolves, that influence also decays. We demonstrated how this influence decay phenomenon is reflected in our model and provided a case study using the Global Terrorism Database

Understanding Behavioral Drivers in Twitter Social Media Networks

As social media platforms facilitate user interactions, organizations increasingly use social media networks (SMNs) to build network ties. Studying user behavior on SMNs can help to uncover strategic information and improve situation awareness. However, there is a lack of understanding of behavioral drivers of SMN participants. This research developed a theoretically-based IS development framework for modeling user behavior in large evolving SMNs. To demonstrate the feasibility of our framework, we developed a proof-of-concept system for simulating user activities in the SMNs of Twitter social communities. Our system models the complex behavioral features in the SMNs by using a wide range of theoretically-driven features and machine-discovered features, and predicts user activities by using a pipeline of statistical and machine-learning techniques. Preliminary results of a simulation study provide insights of the importance of comprehensive network features to model SMN group behavior accurately and quality of commitment features to model SMN user behavior

Effficient Graph-based Computation and Analytics

With data explosion in many domains, such as social media, big code repository, Internet of Things (IoT), and inertial sensors, only 32% of data available to academic and industry is put to work, and the remaining 68% goes unleveraged. Moreover, people are facing an increasing number of obstacles concerning complex analytics on the sheer size of data, which include 1) how to perform dynamic graph analytics in a parallel and robust manner within a reasonable time? 2) How to conduct performance optimizations on a property graph representing and consisting of the semantics of code, data, and runtime systems for big data applications? 3) How to innovate neural graph approaches (ie, Transformer) to solve realistic research problems, such as automated program repair and inertial navigation? To tackle these problems, I present two efforts along this road: efficient graph-based computation and intelligent graph analytics. Specifically, I firstly propose two theory-based dynamic graph models to characterize temporal trends in large social media networks, then implement and optimize them atop Apache Spark GraphX to improve their performances. In addition, I investigate a semantics-aware optimization framework consisting of offline static analysis and online dynamic analysis on a property graph representing the skeleton of a data-intensive application, to interactively and semi-automatically assist programmers to scrutinize the performance problems camouflaged in the source code. In the design of intelligent graph-based algorithms, I innovate novel neural graph-based approaches with multi-task learning techniques to repair a broad range of programming bugs automatically, and also improve the accuracy of pedestrian navigation systems in only consideration of sensor data of Inertial Measurement Units (IMU, ie accelerometer, gyroscope, and magnetometer). In this dissertation, I elaborate on the definitions of these research problems and leverage the knowledge of graph computation, program analysis, and deep learning techniques to seek solutions to them, followed by comprehensive comparisons with the state-of-the-art baselines and discussions on future research

SMART TECHNOLOGIES, DIGITALIZZAZIONE E CAPITALE INTELLETTUALE. Sinergie e opportunità

Il volume offre una visione d’insieme delle smart technologies, della digitalizzazione e del capitale intellettuale nelle aziende, al fine di delinearne i profili emergenti in chiave economico-aziendale. L’analisi di tali tematiche riveste particolare importanza nell’attuale scenario, in cui le aziende sono chiamate ad accogliere la quarta rivoluzione industriale e a fronteggiare un’emergenza mondiale di natura sociosanitaria ed economica. Pertanto, la riflessione scientifica su questioni relative all’analisi e alla definizione delle sfide e delle opportunità derivanti dalle smart technologies, dai processi e percorsi di digitalizzazione aziendali e dal capitale intellettuale rappresenta un contributo fondamentale per supportare le aziende nelle necessarie valutazioni di convenienza, nelle decisioni consapevoli e condivise, e nella attivazione di comportamenti coerenti. I contributi di ricerca raccolti in questo volume rappresentano il fruttuoso lavoro del Gruppo di Studio “Smart Technologies, Digitalization & Intellectual Capital” (STEDIC) della Società Italiana dei Docenti di Ragioneria e di Economia Aziendale (SIDREA) coordinato dai curatori del volume