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Link analysis
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Link analysis can be defined in three ways, depending on discipline or approach. The first has its roots in social network analysis, which borrows from graph theory (the theoretical study of graphs, their structure and application) and sociometry (the study and measurement of relationships within a group of people). Link analysis, within this approach, is a data analysis technique that is used to evaluate connections or relationships between individuals or objects in a network (social networks, semantic networks or conflict networks, for example). It enables researchers to calculate centrality (a measure of the importance or influence of nodes in a network), which includes degree centrality (the number of direct connections or ties), closeness centrality (the average length of the shortest path) and betweenness centrality (the number of times a node occurs on all shortest paths). More information about social network analysis, including definition of key terms such as nodes, ties, arcs, edges and bridges, can be found in Chapter 53. Link analysis, within this approach, has a long pre-digital history. However, it has been included in this book because this type of link analysis is now carried out using powerful computational tools to identify, process, transform, analyse and visualise linkage data (see below and in Chapter 53 for examples of relevant digital tools and software packages).
Unsupervised Learning
Published in Mark Chang, Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare, 2020
In network theory, link analysis is a data-analysis technique used to evaluate relationships (connections) between nodes. Relationships may be identified among various types of nodes (objects), including organizations, people, and transactions. Link analysis has been used in the investigation of criminal activity (fraud detection, counterterrorism, and intelligence), computer security analysis, search engine optimization, market research, medical research, and even in understanding works of art.
Collective Intelligence in Networking
Published in Phan Cong Vinh, Nature-Inspired Networking: Theory and Applications, 2018
In information network analysis, the most well-known ranking algorithm is PageRank [36], which has been successfully applied to the Web search problem. PageRank is a link analysis algorithm that assigns a numerical weight to each object in the information network, with the purpose of “measuring” its relative importance within the object set.
A review of research in illicit supply-chain networks and new directions to thwart them
Published in IISE Transactions, 2021
Rashid Anzoom, Rakesh Nagi, Chrysafis Vogiatzis
Link analysis: Link analysis/prediction involves estimating the likelihood of a link existing between two nodes based on observed ties and attributes of the nodes. Researchers have proposed different algorithms over time for this purpose (see Al Hasan and Zaki (2011) and Lü and Zhou (2011) for details). However, our focus is limited to the studies in illicit (and criminal) networks. Schroeder et al. (2007) discussed four principal methods for criminal link analysis: heuristics-based, template-based, similarity-based, and statistical. Link detection in these methods depend on decision rules, pre-defined template, similarity between entities, and lexical statistics, respectively.