The field of digital libraries (DLs) coalesced in 1994: the first digital library conferences were held that year, awareness of the World Wide Web was accelerating, and the National Science Foundation awarded @math AuthorRank$ as an indicator of the impact of an individual author in the network. The results are validated against conference program committee members in the same period. The results show clear advantages of PageRank and AuthorRank over degree, closeness and betweenness centrality metrics. We also investigate the amount and nature of international participation in Joint Conference on Digital Libraries (JCDL).
Social network analysis is based on the premise that the relationships between social actors can be described by a graph. The graph's nodes represent social actors and the graph's edges connect pairs of nodes and thus represent social interactions. This representation allows researchers to apply graph theory @cite_12 to the analysis of what would otherwise be considered an inherently elusive and poorly understood problem: the tangled web of our social interactions. In this article, we will assume such graph representation and use the terms , , and interchangeably. The terms , , and are also used interchangeably.
@cite_12
1584092895 67385939 1977186326 2045317751
Social network analysis uses techniques from graph theory to analyze the structure of relationships among social actors such as individuals or groups. We investigate the effect of the layout of a social network on the inferences drawn by observers about the number of social groupings evident and the centrality of various actors in the network. We conducted an experiment in which eighty subjects provided answers about three drawings. The subjects were not told that the drawings were chosen from five different layouts of the same graph. We found that the layout has a significant effect on their inferences and present some initial results about the way certain Euclidean features will affect perceptions of structural features of the network. There is no “best” layout for a social network; when layouts are designed one must take into account the most important features of the network to be presented as well as the network itself. Social network analysis is a subdiscipline of the social sciences using graph-theoretic concepts to understand and explain social structure.We describe the main issues in social network analysis. General principles are laid out for visualizing network data in a way that conveys structural information relevant to specific research questions. Based on these innovative graph drawing techniques integrating the analysis and visualization of social networks are introduced. Sociologists since Simmel have been interested in social circles as essential features of friendship networks. Although network analysis has been increasingly used to uncover patterns among social relationships, theoretical explanations of these patterns have been inadequate. This paper presents a theory of the social organization of friendship ties. The approach is based upon Homans's concepts of activities, interactions, and sentiments and upon the concept of extra-network foci organizing social activities and interaction. The theory is contrasted with Heider's balance theory. Implications for transitivity, network bridges, and density of personal networks are discussed and presented as propositions. The focus theory is shown to help explain patterns of friendships in the 1965-66 Detroit Area Study. This paper is intended as a step toward the development of integrated theory to explain interrelationships between networks and other aspects of social structure. Implications for data analysis are discussed. Sociologists have long recognized the importance of patterns in networks of relations that connect individuals with each other. Simmel (1955) described modern society as consisting of loosely connected social circles of relationships. Granovetter (1973) has indicated the general significance of these social circles for communication, community organization, and social conflict. Various studies have supported this picture of the essential patterns in social networks, including Moreno's sociometry (1953), Milgram's "small world" experiments (1967), and Kadushin's observations (1966). Unfortunately, the study of social networks has often been carried out without concern for the origins in the larger social context. Most network analysis ends with description and labeling of patterns; and when explanations of patterns are offered, they frequently rely upon inherent tendencies within networks to become consistent, balanced, or transitive. As a consequence of such atheoretical and or self-contained network theoretical approaches, data are collected and data analysis techniques are devised for Social network analysis is the study of patterns of interaction between social entities. The field is attracting increasing attention from diverse disciplines including sociology, epidemiology, and behavioral ecology. An important sociological phenomenon that draws the attention of analysts is the emergence of communities, which tend to form, evolve, and dissolve gradually over a period of time. Understanding this evolution is crucial to sociologists and domain scientists, and often leads to a better appreciation of the social system under study. Therefore, it is imperative that social network visualization tools support this task. While graph-based representations are well suited for investigating structural properties of networks at a single point in time, they appear to be significantly less useful when used to analyze gradual structural changes over a period of time. In this paper, we present an interactive visualization methodology for dynamic social networks. Our technique focuses on revealing the community structure implied by the evolving interaction patterns between individuals. We apply our visualization to analyze the community structure in the US House of Representatives. We also report on a user study conducted with the participation of behavioral ecologists working with social network datasets that depict interactions between wild animals. Findings from the user study confirm that the visualization was helpful in providing answers to sociological questions as well as eliciting new observations on the social organization of the population under study.