We extend the direct approach for blockmodeling one-mode data to two-mode data. The key idea in this development is that the rows and columns are partitioned simultaneously but in different ways. Many (but not all) of the generalized block types can be mobilized in blockmodeling twomode network data. These methods were applied to some 'voting' data from the 2000-2001 term of the Supreme Court and to the classic Deep South data on women attending events. The obtained partitions are easy to interpret and compelling. The insight that rows and columns can be partitioned in different ways can be applied also to one-mode data. This is illustrated by a partition of a journal-tojournal citation network where journals are viewed simultaneously as both producers and consumers of scientific knowledge.Blockmodeling tools were developed to partition network actors into clusters, called positions, and, at the same time, to partition the set of ties into blocks that are defined by the positions. (See Lorrain and White (1971), Breiger et al. (1975), andBurt (1976) for the foundational statements.) For these authors, and those using their methods, the foundation for the partitioning was structural equivalence. White and Reitz (1983) generalized structural equivalence to regular equivalence as another principle for blockmodeling networks. For all of these authors, the use of blockmodeling tools was inductive in the sense of specifying an equivalence type and searching for partitions that approximated those equivalence types 1 . The procedures were indirect in the sense of converting network data into a (dis)similarity matrix and using some clustering algorithm. Batagelj et al. (1992a,b) suggested an alternative strategy where the partitioning was done by using the network data directly. In essence, their approach was built upon the recognition that both structural and regular equivalence define certain block types if a partition of actors and ties is exact and consistent with the type of equivalence. For structural equivalence, the ideal blocks are null and complete (Batagelj et al. 1992a), and for regular equivalence, the ideal block types are null and regular (Batagelj et al. 1992b). Subsequently, blockmodeling was generalized to permit many new types of blocks. See Batagelj, 1997 andDoreian et al. (1994). The notion of constructing blockmodeling in terms of a larger set of block types, together with the use of optimization methods mobilized within a direct approach has been called generalized blockmodeling (Doreian et al. (2004). Hitherto, these methods have been applied only to one-mode network data. Here, we consider another extension of blockmodeling by including two-mode network data.

We combine two seemingly distinct perspectives regarding the modeling of network dynamics. One perspective is found in the work of physicists and mathematicians who formally introduced the small world model and the mechanism of preferential attachment. The other perspective is sociological and focuses on the process of cumulative advantage and considers the agency of individual actors in a network. We test hypotheses, based on work drawn from these perspectives, regarding the structure and dynamics of scientific collaboration networks. The data we use are for four scientific disciplines in the Slovene system of science. The results deal with the overall topology of these networks and specific processes that generate them. The two perspectives can be joined to mutual benefit. Within this combined approach, the presence of small-world structures was confirmed. However preferential attachment is far more complex than advocates of a single autonomous mechanism claim.

Within the framework of McClelland’s motivational theory, a model of the motivational structure of the migrant personality is proposed. It is argued that those who choose to leave their country of origin have higher achievement and power motivation and lower affiliation motivation than those who want to stay. This model was tested with 1050 college students in Albania, the Czech Republic, and Slovenia. Data were collected between 1993 and 1996. MANOVA analysis confirmed our predictions for the achievement and power motives. Students who wanted to emigrate had higher achievement and power motivation scores than those who wanted to stay. This model was also applied to internal migrants. It was tested with 789 United States college students. Those who wanted to leave the region of their university after graduation scored significantly higher on achievement and power motivation than those who wanted to stay. It is argued that this pattern is specific for countries or regions of economic stagnation or decline, while it may be reversed for countries or regions of economic growth. Predictions for the affiliation motivation were only partly supported. Our findings suggest that psychological factors are important predictors of (e)migration.

This paper examines the collaboration structures and dynamics of the co-authorship network of all Slovenian researchers. Its goal is to identify the key factors driving collaboration and the main differences in collaboration behavior across scientific fields and disciplines. Two approaches to modelling network dynamics are combined in this paper: the small-world model and the mechanism of preferential attachment, also known as the process of cumulative advantage. Stochastic-actor-based modelling of co-authorship network dynamics uses data for the complete longitudinal co-authorship networks for the entire Slovenian scientific community from 1996 to 2010. We confirmed the presence of clustering in all fields and disciplines. Preferential attachment is far more complex than a single global mechanism. There were two clear distinctions regarding collaboration within scientific fields and disciplines. One was that some fields had an internal national saturation inhibiting further collaboration. The second concerned the differential impact of collaboration with scientists from abroad on domestic collaboration. In the natural, technical, medical, and biotechnical sciences, this promotes collaboration within the Slovenian scientific community while in the social sciences and humanities this inhibits internal collaboration.

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Scientific collaboration, Coauthorship network, Bibliometry, Longitudinal network analysis, Blockmodeling, Core-periphery structure, Mathematics, Physics, Biotechnology, Sociology,

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