Mining Static and Dynamic Structural Patterns in Networks for Knowledge Management: a Computational Framework and Case studies

Contemporary organizations live in an environment of networks: internally, they manage the networks of employees, information resources, and knowledge assets to enhance productivity and improve efficiency; externally, they form alliances with strategic partners, suppliers, buyers, and other stakeholders to conserve resources, share risks, and gain market power. Many managerial and strategic decisions are made by organizations based on their understanding of the structure of these networks. This dissertation is devoted to network structure mining, a new research topic on knowledge discovery in databases (KDD) for supporting knowledge management and decision making in organizations. A comprehensive computational framework is developed to provide a taxonomy and summary of the theoretical foundations, major research questions, methodologies, techniques, and applications in this new area based on extensive literature review. Research in this new area is categorized into static structure mining and dynamic structure mining. The major research questions of static mining are locating critical resources in networks, reducing network complexity, and capturing topological properties of large-scale networks. An inventory of techniques developed in multiple reference disciplines such as social network analysis and Web mining are reviewed. These techniques have been used in mining networks in various applications including knowledge management, marketing, Web mining, and intelligence and security. Dynamic pattern mining is concerned with network evolution and major findings are reviewed.