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The growth in studying complexity, governance, and networks is a noticeable fact. Complex public policy problems require productive collaborations among multiple actors from different sectors. Public, private, and not-for-profit organizations work through governance networks and collaborative governance to solve complex public policy problems. There is a strong interest in both the practice and theory of networks in public governance. This interest has given boost to the use of social network analysis (SNA) in studies on complex governance networks. This special issue of Complexity, Governance, & Networks, includes theoretically, conceptually, and analytically rigorous papers with social network analysis applications. The papers help our understanding in studying complex governance networks.

To analyze social network data using standard statistical approaches is to risk incorrect inference. The dependencies among observations implied in a network conceptualization undermine standard assumptions of the usual general linear models. One of the most quickly expanding areas of social and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs), and stochastic actor‐oriented models. We focus most attention on ERGMs by providing an illustrative example of a model for a strategic information network within a local government. We draw inferences about the structural role played by individuals recognized as key innovators and conclude that such an approach has much to offer in analyzing the policy process.

AbstractWe consider three network disconnection problems in a centralized network where a source node provides service to the other nodes, called demand nodes. In network disconnection problems, each demand node gets a certain benefit when connected to a source node and a network attacker destroys edges to prevent demand nodes from achieving benefits. As destroying edges incurs expenses, an attacker considers the following three different strategies. The first is to maximize the sum of benefits of the disconnected nodes while keeping the total edge destruction cost no more than a given budget. The second is to minimize the total destruction cost needed to make a certain amount of benefits not accomplished. The last is to minimize the ratio of the total destruction cost to the benefits not accomplished. In this paper, we develop exact algorithms to solve the above three problems. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

Discusses the value of social network analysis for teasing out the network processes in social movements, particularly (1) how collective action is affected by actors' embeddedness in preexisting networks, & (2) how actors create new linkages that shape the subsequent development of protest &/or subcultural activities. Issues of data collection & organization are examined in detail, & ways of analyzing social movement structure & individual positions in social movement networks are demonstrated. A combination of qualitative & quantitative methods is deemed the most useful in network analysis. 5 Tables, 3 Figures, 74 References. K. Hyatt Stewart