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

The aggregated citation relations among journals included in the Science Citation Index provide us with a huge matrix which can be analyzed in various ways. Using principal component analysis or factor analysis, the factor scores can be used as indicators of the position of the cited journals in the citing dimensions of the database. Unrotated factor scores are exact, and the extraction of principal components can be made stepwise since the principal components are independent. Rotation may be needed for the designation, but in the rotated solution a model is assumed. This assumption can be legitimated on pragmatic or theoretical grounds. Since the resulting outcomes remain sensitive to the assumptions in the model, an unambiguous classification is no longer possible in this case. However, the factor-analytic solutions allow us to test classifications against the structures contained in the database. This will be demonstrated for the delineation of a set of biochemistry journals.

Methods developed for mapping the journal structure contained in aggregated journal-journal citations in the Science Citation Index are applied to the Chinese Science Citation Database of the Chinese Academy of Sciences. This database covered 991 journals in 2001, of which only 37 had originally English titles and only 31 were covered by the SCI. Using factor-analytical and graph-analytical techniques we show that the journal relations are dually structured. The main structure is the intellectual organization of the journals in journal groups (as in the international SCI), but the university-based journals provide an institutional layer that orients this structure towards practical ends (e.g., agriculture). This mechanism of integration is further distinguished from the role of “general science journals. ” The Chinese Science Citation Database thus exhibits the characteristics of “Mode 2 ” in the production of scientific knowledge more than its western counterparts. The contexts of application lead to correlation among the components.

Routability or wiring congestion in a VLSI chip is becoming increasingly important as chip complexity increases. Congestion has a significant impact on performance, yield and chip area. Although advances in placement algorithms have attempted to alleviate this problem, the inherent structure of the logic netlist has a significant impact on the routability irrespective of the placement algorithm used. Placement algorithms find optimal assignment of locations to the logic and do not have the ability to change the netlist structure. Significant decisions regarding the circuit structure are made early in synthesis such as during the technology independent logic optimization step. Optimizations in this step use literal count as a metric for optimization and do not adequately capture the intrinsic entanglement of the netlist. Two circuits with identical literal counts may have significantly different congestion characteristics post placement. In this paper, we motivate that a property of the network structure called adhesion can make a significant contribution to routing congestion. We then provide a metric to measure this property. We also show that adhesion as measured by this metric can be used in addition to literal counts to estimate and optimize post routing congestion early in the design flow. 1.

We examine data on and models of small world properties and parameters of social networks. Our focus, on tie-strength, multilevel networks and searchability in strong-tie social networks, allows us to extend some of the questions and findings of recent research and the fit of small world models to sociological and anthropological data on human communities. We offer a ‘navigability of strong ties ’ hypothesis about network topologies tested with data from kinship systems, and potentially applicable to corporate cultures and business networks. Small Worlds A small world (SW) is a (large) graph with both local clustering and, on average, short distances between nodes [44,45]. Short distances promote accessibility, while local clustering and redundancy of edges, as some research suggests [38,48], promotes robustness to disconnection and, through multiple independent pathways, reliable accessibility as well. For paths to transmit materials and information via network traversal, a small world also requires navigability. This was the property investigated in the first small world experiment by Travers and Milgram [42]: Could people randomly selected in Omaha, Nebraska, successfully send letters to a predetermined target in Boston, when asked to direct their letters to single acquaintances who are asked in turn

Social scientists have shown an increasing interest in understanding the structure of knowledge communities, and particularly the organization of “epistemic communities”, that is groups of agents sharing common knowledge concerns. However, most existing approaches are based only on either social relationships or semantic similarity, while there has been roughly no attempt to link social and semantic aspects. In this paper, we introduce a formal framework addressing this issue and propose a method based on Galois lattices (or concept lattices) for categorizing epistemic communities in an automated and hierarchically structured fashion. Suggesting that our process allows us to rebuild a whole community structure and taxonomy, and notably fields and subfields gathering a certain proportion of agents, we eventually apply it to empirical data to exhibit these alleged structural properties, and successfully compare our results with categories spontaneously given by domain experts.

In this paper, we introduce a method for analyzing the temporal dynamics of affiliation networks. We define affiliation groups which describe temporally related subsets of actors and describe an approach for exploring changing memberships in these affiliation groups over time. To model the dynamic behavior in these networks, we consider the concept of loyalty and introduce a measure that captures an actor’s loyalty to an affiliation group as the degree of ‘commitment ’ an actor shows to the group over time. We evaluate our measure using three real world affiliation networks: a publication network, a senate bill cosponsorship network and a dolphin network. The results show the utility of our measure for analyzing the dynamic behavior of actors and quantifying their loyalty to different time-varying affiliation groups. 1

Abstract. The design and construction of navigation menus for websites have traditionally been performed manually according to the intuition of a web developer. This paper introduces a new approach, FcAWN (pronounced “fawn”) – Formal concept Analysis for Web Navigation – to assist in the design and generation of a coherent and logical navigation hierarchy for a set of web documents. We provide an algorithmic process for generating multi-layered menu models using FcAWN and demonstrate its feasibility with an experimental case study. Our study reveals a fundamental difference between the traditional tree-based menu structure and the lattice-based menu structure by FcAWN: a FcAWN-generated lattice structure is more general than a tree structure and yet is mathematically sound and uniquely suited for menu design and construction. FcAWN is the first mathematical principle for menu design and generation, providing a practical basis for human-computer interaction. 1

Summary. Investing in radical innovation requires specific forms of investment, which have to deal with knightian uncertainty. This paper focuses on the links developed by venture capitalists between them, through co-investment operation, and studies their relationship through social network analysis. We analyze the influence of different proximities (essentially spatial, political and industrial) on the collaboration among the different actors. 1

The vision of the Scientific Web is to enable research scientists to collaborate in a ubiquitous manner; sharing resources, data, and insights how to perform experiments. The grid provides the underlying computing infrastructure, thereby enabling workload sharing and distribution of computational tasks among millions of nodes. However, current collaboration tools in scientific grid environments do not support scientists in managing various resources, artifacts, data, experiments in a team-aware manner. In this paper we introduce an approach to enhance current grid infrastructures with awareness of team interactions. Our proposed activity model allows users to manage activities, involved team members and their expertise, as well as associated grid resources. Based on the action data captured from various sources, we establish a bipartite activity-user graph — central to team-centric collaboration metrics. We discuss the applicability of these metrics in grid environments by introducing novel adaptation algorithms enabling team-aware resource utilization. 1