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157
A Relational View of Information Seeking and Learning in Social Networks
, 2003
"... Research in organizational learning has demonstrated processes and occasionally performance implications of acquisition of declarative (knowwhat) and procedural (knowhow) knowledge. However, considerably less attention has been paid to learned characteristics of relationships that affect the decis ..."
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Cited by 95 (3 self)
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Research in organizational learning has demonstrated processes and occasionally performance implications of acquisition of declarative (knowwhat) and procedural (knowhow) 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.
Centrality and Network Flow
"... Centrality measures, or at least our interpretations of these measures, make implicit assumptions about the manner in which things flow through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest p ..."
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Cited by 77 (2 self)
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Centrality measures, or at least our interpretations of these measures, make implicit assumptions about the manner in which things flow through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest possible paths. This paper lays out a typology of network flows based on two dimensions of variation, namely, the kinds of trajectories that traffic may follow (geodesics, paths, trails or walks), and the method of spread (broadcast, serial replication, or transfer). Measures of centrality are then matched to the kinds of flows they are appropriate for. Simulations are used to examine the relationship between type of flow and the differential importance of nodes with respect to key measurements such as speed of reception of traffic and frequency of receiving traffic. It is shown that the offtheshelf formulas for centrality measures are fully applicable only for the specific flow processes they are designed for, and that when they are applied to other flow processes they get the “wrong” answer. It is noted that the most commonly used centrality measures are not appropriate for most of the flows we are routinely interested in. A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes (such as speed and frequency of reception) given implicit models of how things flow.
Whom you know matters: Venture capital networks and investment performance
 Journal of Finance
, 2007
"... Many financial markets are characterized by strong relationships and networks, rather than arm’slength, spotmarket transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company investments. VC fi ..."
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Cited by 71 (3 self)
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Many financial markets are characterized by strong relationships and networks, rather than arm’slength, spotmarket transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company investments. VC firms that enjoy more influential network positions have significantly better fund performance, as measured by the proportion of investments that are successfully exited through an IPO or sale to another company. Similarly, the portfolio companies of betternetworked VC firms are significantly more likely to survive to subsequent financing and to eventual exit. Finally, we provide initial evidence on the evolution of VC networks.
Centrality in valued graphs: A measure of betweenness based on network flow
, 1991
"... A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and nonvalued graphs. This makes C, applic ..."
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Cited by 66 (7 self)
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A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and nonvalued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network.
Network Analysis Of Knowledge Construction In Asynchronous Learning Networks
, 2003
"... 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 ..."
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Cited by 56 (7 self)
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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 threemonthlong 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 nonstructured 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 teacherfollowers; and the tutor was at the center of activity. These differences are statistically significant. We conclude that a welldesigned ALN develops significant, distinct cohesion, and role and power structures lead the knowledge construction process to high phases of critical thinking.
Network Position and Firm Performance: Organizational Returns to Collaboration
 in the Biotechnology Industry, Research in the Sociology of Organizations, JAI Press Inc
, 1999
"... Powell and K.W. Koput, CoPIs. We thank Steven Andrews, Charles Kadushin, and Arne Kalleberg for helpful comments on an earlier draft. We examine the relationship between position in a network of relationships and organizational performance. Drawing on ten years of observations (19881997) for nearl ..."
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Cited by 42 (4 self)
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Powell and K.W. Koput, CoPIs. We thank Steven Andrews, Charles Kadushin, and Arne Kalleberg for helpful comments on an earlier draft. We examine the relationship between position in a network of relationships and organizational performance. Drawing on ten years of observations (19881997) for nearly 400 firms in the human biotechnology industry, we utilize three types of panel regressions to unravel the complex linkages between network structure, patenting, and various firmlevel outcome measures. Our results highlight the critical role of collaboration in determining the competitive advantage of individual biotech firms and in driving the evolution of the industry. We also find that there are decreasing returns to network experience and diversity, suggesting that there are limits to the learning that occurs through interorganizational networks. 1
Fast Approximation of Centrality
 Journal of Graph Algorithms and Applications
, 2001
"... Social studies researchers use graphs to model group activities in social networks. An important property in this context is the centrality of a vertex: the inverse of the average distance to each other vertex. We describe a randomized approximation algorithm for centrality in weighted graphs. For g ..."
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Cited by 38 (0 self)
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Social studies researchers use graphs to model group activities in social networks. An important property in this context is the centrality of a vertex: the inverse of the average distance to each other vertex. We describe a randomized approximation algorithm for centrality in weighted graphs. For graphs exhibiting the small world phenomenon, our method estimates the centrality of all vertices with high probability within a (1 + #) factor in nearlinear time. 1 Introduction In social network analysis, the vertices of a graph represent agents in a group and the edges represent relationships, such as communication or friendship. The idea of applying graph theory to analyze the connection between the structural centrality and group process was introduced by Bavelas [4]. Various measurement of centrality [7, 14, 15] have been proposed for analyzing communication activity, control, or independence within a social network. We are particularly interested in closeness centrality [5, 6, 24]...
Tweet the debates: Understanding community annotation of uncollected sources
 In WSM ’09: Proceedings of the international workshop on Workshop on Social
, 2009
"... We investigate the practice of sharing short messages (microblogging) around live media events. Our focus is on Twitter and its usage during the 2008 Presidential Debates. We find that analysis of Twitter usage patterns around this media event can yield significant insights into the semantic structu ..."
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Cited by 38 (6 self)
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We investigate the practice of sharing short messages (microblogging) around live media events. Our focus is on Twitter and its usage during the 2008 Presidential Debates. We find that analysis of Twitter usage patterns around this media event can yield significant insights into the semantic structure and content of the media object. Specifically, we find that the level of Twitter activity serves as a predictor of changes in topics in the media event. Further we find that conversational cues can identify the key players in the media object and that the content of the Twitter posts can somewhat reflect the topics of discussion in the media object, but are mostly evaluative, in that they express the poster’s reaction to the media. The key contribution of this work is an analysis of the practice of microblogging live events and the core metrics that can leveraged to evaluate and analyze this activity. Finally, we offer suggestions on how our model of segmentation and node identification could apply towards any live, realtime arbitrary event.
Centrality estimation in large networks
 INTL. JOURNAL OF BIFURCATION AND CHAOS, SPECIAL ISSUE ON COMPLEX NETWORKS’ STRUCTURE AND DYNAMICS
, 2007
"... Centrality indices are an essential concept in network analysis. For those based on shortestpath distances the computation is at least quadratic in the number of nodes, since it usually involves solving the singlesource shortestpaths (SSSP) problem from every node. Therefore, exact computation is ..."
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Cited by 30 (0 self)
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Centrality indices are an essential concept in network analysis. For those based on shortestpath distances the computation is at least quadratic in the number of nodes, since it usually involves solving the singlesource shortestpaths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.