This paper is dedicated to Philip J. Stone III, who first put me online in 1965.

Sociological investigations of economic exchange reveal how institutions and social structures shape transaction patterns among economic actors. This article explores how interfirm networks in the U.S. venture capital (VC) market affect spatial patterns of exchange. Evidence suggests that information about potential investment opportunities generally circulates within geographic and industry spaces. In turn, the circumscribed flow of information within these spaces contributes to the geographic- and industry-localization of VC investments. Empirical analyses demonstrate that the social networks in the VC community—built up through the industry’s extensive use of syndicated investing—diffuse information across boundaries and therefore expand the spatial radius of exchange. Venture capitalists that build axial positions in the industry’s coinvestment network invest more frequently in spatially distant companies. Thus, variation in actors ’ positioning within the structure of the market appears to differentiate market participants ’ ability to overcome boundaries that otherwise would curtail exchange.

How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M : N relation in database terminology can be represented as a graph. A lot of these questions boil down to the following: "How can we generate synthetic but realistic graphs?" To answer this, we must first understand what patterns are common in real-world graphs and can thus be considered a mark of normality/realism. This survey give an overview of the incredible variety of work that has been done on these problems. One of our main contributions is the integration of points of view from physics, mathematics, sociology, and computer science. Further, we briefly describe recent advances on some related and interesting graph problems.

This paper argues that the value of external knowledge sharing increases when work groups are more structurally diverse. A structurally diverse work group is one in which the members, by virtue of their different organizational affiliations, roles, or positions, can expose the group to unique sources of knowledge. It is hypothesized that if members of structurally diverse work groups engage in external knowledge sharing, their performance will improve because of this active exchange of knowledge through unique external sources. A field study of 182 work groups in a Fortune 500 telecommunications firm operationalizes structural diversity as member differences in geographic locations, functional assignments, reporting managers, and business units, as indicated by corporate database records. External knowledge sharing was measured with group member surveys and performance was assessed using senior executive ratings. Ordered logit analyses showed that external knowledge sharing was more strongly associated with performance when work groups were more structurally diverse. Implications for theory and practice around the integration of work groups and social networks are addressed

where these ideas were initially discussed and much of the work was done. We are especially grateful to John Padgett, organizer of the States and Markets group at SFI for his support and insights. We have benefited from comments from the audience at seminars

Recent research suggests that people obtain useful knowledge from others with whom they work closely and frequently (i.e., strong ties). Yet there has been limited empirical work examining why this is so. Moreover, other research suggests that weak ties provide useful knowledge. To help integrate these multiple findings, 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 trust dimensions, the structural benefit of weak ties became visible. This latter finding is consistent with prior research suggesting that weak ties provide access to non-redundant information. Third, we found that competence-based trust was especially important for the receipt of tacit knowledge. We discuss implications for theory and practice. 2

Asynchronous Learning Networks (ALNs) make the process of collaboration more transparent, because a transcript of conference messages can be used to assess individual roles and contributions and the collaborative process itself. This study considers three aspects of ALNs: the design; the quality of the resulting knowledge construction process; and cohesion, role and power network structures. The design is evaluated according to the Social Interdependence Theory of Cooperative Learning. The quality of the knowledge construction process is evaluated through Content Analysis; and the network structures are analyzed using Social Network Analysis of the response relations among participants during online discussions. In this research we analyze data from two three-monthlong ALN academic university courses: a formal, structured, closed forum and an informal, nonstructured, open forum. We found that in the structured ALN, the knowledge construction process reached a very high phase of critical thinking and developed cohesive cliques. The students took on bridging and triggering roles, while the tutor had relatively little power. In the non-structured ALN, the knowledge construction process reached a low phase of cognitive activity; few cliques were constructed; most of the students took on the passive role of teacher-followers; and the tutor was at the center of activity. These differences are statistically significant. We conclude that a well-designed ALN develops significant, distinct cohesion, and role and power structures lead the knowledge construction process to high phases of critical thinking.

Research in organizational learning has demonstrated processes and occasionally performance implications of acquisition of declarative (know-what) and procedural (know-how) knowledge. However, considerably less attention has been paid to learned characteristics of relationships that affect the decision to seek information from other people. Based on a review of the social network, information processing, and organizational learning literatures, along with the results of a previous qualitative study, we propose a formal model of information seeking in which the probability of seeking information from another person is a function of (1) knowing what that person knows; (2) valuing what that person knows; (3) being able to gain timely access to that person’s thinking; and (4) perceiving that seeking information from that person would not be too costly. We also hypothesize that the knowing, access, and cost variables mediate the relationship between physical proximity and information seeking. The model is tested using two separate research sites to provide replication. The results indicate strong support for the model and the mediation hypothesis (with the exception of the cost variable). Implications are drawn for the study of both transactive memory and organizational learning, as well as for management practice.

Most personal (egocentric) network studies describe networks using measures that are not structural, opting instead for attribute-based analyses that summarize the relationships of the respondent to network members. Those researchers that have used structural measures have done so on networks of less than 10 members who represent the network core. Although much has been learned by focusing on attribute-based analyses of personal network data, the application of structural analyses that are traditionally used on whole (sociocentric) network data may prove fruitful. The utility of this approach becomes apparent when the sample of network members elicited is relatively large. Forty six respondents free-listed 60 network members and evaluated tie strength between all 1,770 unique pairs of members. Graph-based measures of cohesion and subgroups revealed variability in the personal network structure. Non-hierarchical clustering generated subgroups which were subsequently verified by respondents as meaningful. Further analysis of the correlation between subgroup types and overlap between subgroups demonstrates how the analysis of each network can be summarized across subjects. Four case studies are presented to illustrate the richness of the data and the value of contrasting individual matrix results to the norm as defined by all 45 subjects.

for access to the data on MIT patents and for answering many questions about the data and