Book•
Social Network Analysis: Methods and Applications
25 Nov 1994-
TL;DR: This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of Dyadic and Triadic Interaction Models, which describes the relationships between actor and group measures and the structure of networks.
Abstract: Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
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TL;DR: In this paper, the authors show that the probability of a new alliance between specific organi-zations increases with their interdependence and also with their prior mutual alliances, common third parties, and joint centrality in the alliance network.
Abstract: Organizations enter alliances with each other to access critical re‐sources, but they rely on information from the network of prior alli‐ances to determine with whom to cooperate. These new alliances modify the existing network, prompting an endogenous dynamic be‐tween organizational action and network structure that drives the emergence of interorganizational networks. Testing these ideas on alliances formed in three industries over nine years, this research shows that the probability of a new alliance between specific organi‐zations increases with their interdependence and also with their prior mutual alliances, common third parties, and joint centrality in the alliance network. The differentiation of the emerging network structure, however, mitigates the effect of interdependence and en‐hances the effect of joint centrality on new alliance formation.
2,864 citations
21 Aug 2005
TL;DR: Differences in the behavior of liberal and conservative blogs are found, with conservative blogs linking to each other more frequently and in a denser pattern.
Abstract: In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. Specifically, we analyze the posts of 40 "A-list" blogs over the period of two months preceding the U.S. Presidential Election of 2004, to study how often they referred to one another and to quantify the overlap in the topics they discussed, both within the liberal and conservative communities, and also across communities. We also study a single day snapshot of over 1,000 political blogs. This snapshot captures blogrolls (the list of links to other blogs frequently found in sidebars), and presents a more static picture of a broader blogosphere. Most significantly, we find differences in the behavior of liberal and conservative blogs, with conservative blogs linking to each other more frequently and in a denser pattern.
2,800 citations
TL;DR: A model of two-party (dyadic) knowledge exchange is proposed and test, with strong support in each of the three companies surveyed, and the link between strong ties and receipt of useful knowledge was mediated by competence- and benevolence-based trust.
Abstract: Research has demonstrated that relationships are critical to knowledge creation and transfer, yet findings have been mixed regarding the importance of relational and structural characteristics of social capital for the receipt of tacit and explicit knowledge. We propose and test a model of two-party (dyadic) knowledge exchange, with strong support in each of the three companies surveyed. First, the link between strong ties and receipt of useful knowledge (as reported by the knowledge seeker) was mediated by competence- and benevolence-based trust. Second, once we controlled for these two trustworthiness dimensions, the structural benefit ofweak ties emerged. This finding is consistent with prior research suggesting that weak ties provide access to nonredundant information. Third, competence-based trust was especially important for the receipt of tacit knowledge. We discuss implications for theory and practice.
2,649 citations
TL;DR: It is found that the most accurate methods tend to be more computationally expensive, and that both aspects need to be considered when choosing a method for practical purposes.
Abstract: We compare recent approaches to community structure identification in terms of sensitivity and computational cost. The recently proposed modularity measure is revisited and the performance of the methods as applied to ad hoc networks with known community structure, is compared. We find that the most accurate methods tend to be more computationally expensive, and that both aspects need to be considered when choosing a method for practical purposes. The work is intended as an introduction as well as a proposal for a standard benchmark test of community detection methods.
2,630 citations
Cites background from "Social Network Analysis: Methods an..."
...finition of community must be purely topological. Social networkshasbeen the subjectofinterest forsociologists fordecades. 2 For a standard text on the social science approach to networks analysis see [22]. The social science approach is largely (though by no means exclusively) concerned with the effect an individual player has on the network and vice versa. As a result, the local properties of networks...
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01 Jan 2006
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Abstract: The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.
2,591 citations