Author
Stephen B. Seidman
Other affiliations: Texas State University, George Mason University, Colorado State University ...read more
Bio: Stephen B. Seidman is an academic researcher from University of Central Arkansas. The author has contributed to research in topics: Social software engineering & Software engineering professionalism. The author has an hindex of 14, co-authored 49 publications receiving 3196 citations. Previous affiliations of Stephen B. Seidman include Texas State University & George Mason University.
Papers
More filters
TL;DR: An approach to network cohesion is proposed that is based on minimum degree and which produces a sequence of subgraphs of gradually increasing cohesion that associates with any network measures of local density which promise to be useful both in characterizing network structures and in comparing networks.
1,730 citations
TL;DR: A family of new clique‐like structures is proposed which captures an aspect of cliques which is seldom treated in the existing literature and provides a new means of tapping several important properties of social networks.
Abstract: For at least twenty‐five years, the concept of the clique has had a prominent place in sociometric and other kinds of sociological research. Recently, with the advent of large, fast computers and with the growth of interest in graph‐theoretic social network studies, research on the definition and investigation of the graph theoretic properties of clique‐like structures has grown. In the present paper, several of these formulations are examined, and their mathematical properties analyzed. A family of new clique‐like structures is proposed which captures an aspect of cliques which is seldom treated in the existing literature. The new structures, when used to complement existing concepts, provide a new means of tapping several important properties of social networks.
450 citations
01 Dec 2007
TL;DR: This paper collects and classifies research that gives well-supported advice to computing academics teaching introductory programming, and identifies important work that mediates it to computing educators and professional bodies.
Abstract: Three decades of active research on the teaching of introductory programming has had limited effect on classroom practice. Although relevant research exists across several disciplines including education and cognitive science, disciplinary differences have made this material inaccessible to many computing educators. Furthermore, computer science instructors have not had access to a comprehensive survey of research in this area. This paper collects and classifies this literature, identifies important work and mediates it to computing educators and professional bodies.We identify research that gives well-supported advice to computing academics teaching introductory programming. Limitations and areas of incomplete coverage of existing research efforts are also identified. The analysis applies publication and research quality metrics developed by a previous ITiCSE working group [74].
434 citations
TL;DR: The authors used graph-theoretic social network techniques to examine interpersonal relationships and brand choice behavior in natural environments, and found significant brand congruence effects were obtained, they were clustered in a few products mediated by types of social relation.
Abstract: Previous studies dealing with the notion of brand congruence suffer from questionable methods of group determination, suspect demonstrations of brand congruence effects, and inadequate attention paid to types of social relation. To overcome these shortcomings, the present study uses graph-theoretic social network techniques to examine interpersonal relationships and brand choice behavior in natural environments. The brand choices of individuals in a social relationship were compared to those of unrelated individuals across various products, types of social relation, and types of basic sociological structure (dyad, clique, and 2-plex). While significant brand congruence effects were obtained, they were clustered in a few products mediated by types of social relation. Conspicuousness of the product, as traditionally defined, was found to be insufficient to account for these findings.
204 citations
TL;DR: The hull number ( h ) of a graph is the cardinality of the smallest set of points whose convex hull is the entire graph, which is the smallest convex set that contains T.
117 citations
Cited by
More filters
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
TL;DR: A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear.
Abstract: Complex networks arise in a wide range of biological and sociotechnical systems. Epidemic spreading is central to our understanding of dynamical processes in complex networks, and is of interest to physicists, mathematicians, epidemiologists, and computer and social scientists. This review presents the main results and paradigmatic models in infectious disease modeling and generalized social contagion processes.
3,173 citations
TL;DR: This paper showed that the most efficient spreaders are not always necessarily the most connected agents in a network, and that the position of an agent relative to the hierarchical topological organization of the network might be as important as its connectivity.
Abstract: Spreading of information, ideas or diseases can be conveniently modelled in the context of complex networks. An analysis now reveals that the most efficient spreaders are not always necessarily the most connected agents in a network. Instead, the position of an agent relative to the hierarchical topological organization of the network might be as important as its connectivity.
2,618 citations