Author
Phillip Bonacich
Other affiliations: Stanford University, University of California, Berkeley
Bio: Phillip Bonacich is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Centrality & Katz centrality. The author has an hindex of 28, co-authored 65 publications receiving 10700 citations. Previous affiliations of Phillip Bonacich include Stanford University & University of California, Berkeley.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, the rank orderings by the four networks whose analysis forms the heart of this paper were analyzed and compared to the rank ordering by the three centrality measures, i.e., betweenness, nearness, and degree.
Abstract: 2In an influential paper, Freeman (1979) identified three aspects of centrality: betweenness, nearness, and degree. Perhaps because they are designed to apply to networks in which relations are binary valued (they exist or they do not), these types of centrality have not been used in interlocking directorate research, which has almost exclusively used formula (2) below to compute centrality. Conceptually, this measure, of which c(ot, 3) is a generalization, is closest to being a nearness measure when 3 is positive. In any case, there is no discrepancy between the measures for the four networks whose analysis forms the heart of this paper. The rank orderings by the
4,482 citations
TL;DR: In this paper, Factoring and weighting approaches to status scores and clique identification were proposed, and the results showed that the weighting approach is more accurate than the factoring approach.
Abstract: (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology: Vol. 2, No. 1, pp. 113-120.
2,661 citations
TL;DR: Eigenvectors, and the related centrality measure Bonacich's c(β), have advantages over graph-theoretic measures like degree, betweenness, and closeness centrality: they can be used in signed and valued graphs and the beta parameter in c( β) permits the calculation of power measures for a wider variety of types of exchange.
1,122 citations
TL;DR: An alternative measure of centrality is suggested that equals an eigenvector when eigenvectors can be used and provides meaningfully comparable results when they cannot.
967 citations
TL;DR: A sociological concern with the pattern of overlapping memberships is discussed in this paper, which can give information about the power centralization in a society, as well as the sharing of common members is an important relationship.
Abstract: Sociologists study the structure or pattern of relationships among individuals and among groups. The sharing of common members is an important relationship, as is the pattern of overlapping members. I will first discuss few instances of sociological concern with the pattern of overlapping memberships in order to clarify the problems that this chapter will solve. The pattern of interlocking directorates among business organizations can give information about the power centralization in a society. Lieberson (1971) suggests that an analysis of the pattern of interlocking directorates among the largest business organizations sheds light on whether the power-elite view or the pluralist view of American society is the more accurate. Lieberson confines himself to a few selected facts (for instance, that the boards of the seven largest New York City banks in 1965 included officials from 51 of the largest 500 industrial companies). Although suggesting the value of a thorough analysis of interlocking directorates, Lieberson does not attempt to provide it.
333 citations
Cited by
More filters
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
17,647 citations
TL;DR: In this paper, a definition of trust and a model of its antecedents and outcomes are presented, which integrate research from multiple disciplines and differentiate trust from similar constructs, and several research propositions based on the model are presented.
Abstract: Scholars in various disciplines have considered the causes, nature, and effects of trust. Prior approaches to studying trust are considered, including characteristics of the trustor, the trustee, and the role of risk. A definition of trust and a model of its antecedents and outcomes are presented, which integrate research from multiple disciplines and differentiate trust from similar constructs. Several research propositions based on the model are presented.
16,559 citations
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
9,441 citations
TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
9,057 citations
TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
8,432 citations