The web is central to many human activities and infringes on many others: at home, and at work, including education and research. Links between web sites can be used in information science and social science research as a valuable source of evidence about online phenomena, and about online components of offline phenomena. Given a set of websites, the links between them many reveal interesting patterns of connectedness that could reflect issues of underlying human communication or information value. Link analysis is therefore a valuable tool for information science and social science researchers investigating the web, or other phenomena with an offline component. This book provides methods, guidelines and examples to guide researchers and students through a research project, in addition to reviewing a considerable body of previous work.
- Contains a complete link analysis methodology for information science and social science research.
- A strong online component includes software and instructions to allow complete link analyses (http: //linkanalysis.wlv.ac.uk).
- Case studies include academic, business and commercial search engine applications.
- Practical chapters describe a range of related methods, software and data sources.

This book brings together multi-disciplinary research and practical evidence about the role and exploitation of big data in driving and supporting innovation in tourism. It also provides a consolidated framework and roadmap summarising the major issues that both researchers and practitioners have to address for effective big data innovation. The book proposes a process-based model to identify and implement big data innovation strategies in tourism. This process framework consists of four major parts: 1) inputs required for big data innovation; 2) processes required to implement big data innovation; 3) outcomes of big data innovation; and 4) contextual factors influencing big data exploitation and advances in big data exploitation for business innovation.

This book brings together multi-disciplinary research and practical evidence about the role and exploitation of big data in driving and supporting innovation in tourism. It also provides a consolidated framework and roadmap summarising the major issues that both researchers and practitioners have to address for effective big data innovation. The book proposes a process-based model to identify and implement big data innovation strategies in tourism. This process framework consists of four major parts: 1) inputs required for big data innovation; 2) processes required to implement big data innovation; 3) outcomes of big data innovation; and 4) contextual factors influencing big data exploitation and advances in big data exploitation for business innovation.