THE EFFECTS OF NETWORK’S STRUCTURAL HOLES: POLYCENTRIC INSTITUTIONS, PRODUCT PORTFOLIO, AND NEW VENTURE GROWTH IN CHINA AND RUSSIA

This study examines the effect of network’s structural holes, i.e., the absence of a link between two contacts who are both linked to an actor, on product development and profit growth of software ventures in two different institutional contexts of China and Russia. Using interview data of 159 software entrepreneurs in Beijing and Moscow, the study found that the effect of structural holes is contingent upon country institutional context and venture development stage. Specifically, structural holes have a positive main effect on product portfolio but a negative main effect on profit growth in the second revenue year - early stage of venture development. Structural holes are more useful in the Russian institutional context compared to the Chinese institutional context due to the polycentricity of institutions. The research implications of the findings are discussed.http://deepblue.lib.umich.edu/bitstream/2027.42/133050/1/wp1033.pd

SOFTWARE ENTREPRENEURSHIP: KNOWLEDGE NETWORKS AND PERFORMANCE OF SOFTWARE VENTURES IN CHINA AND RUSSIA

This study examines the impact of entrepreneurs’ network structure and knowledge homogeneity/heterogeneity of their network members on product development, and revenue growth of software ventures in China and Russia. The empirical data are composed of structured interviews with 159 software entrepreneurs in Beijing and Moscow. The study found that structural holes and knowledge heterogeneity affect positively product diversity in interactive ways. The study also found that knowledge homogeneity accelerates product development. Product development speed enhances revenue growth in the long term. However, the combination of speed with dense and homogeneous networks harms revenue growth over time. The effects of structural holes and knowledge heterogeneity on product diversity and revenue growth over time are more salient in Russia due to the unique institutional, social, and cultural conditions present in the country.http://deepblue.lib.umich.edu/bitstream/2027.42/40137/3/wp751.pd

SOFTWARE ENTREPRENEURSHIP: KNOWLEDGE NETWORKS AND PERFORMANCE OF SOFTWARE VENTURES IN CHINA AND RUSSIA

This study examines the impact of entrepreneurs’ network structure and knowledge homogeneity/heterogeneity of their network members on product development, and revenue growth of software ventures in China and Russia. The empirical data are composed of structured interviews with 159 software entrepreneurs in Beijing and Moscow. The study found that structural holes and knowledge heterogeneity affect positively product diversity in interactive ways. The study also found that knowledge homogeneity accelerates product development. Product development speed enhances revenue growth in the long term. However, the combination of speed with dense and homogeneous networks harms revenue growth over time. The effects of structural holes and knowledge heterogeneity on product diversity and revenue growth over time are more salient in Russia due to the unique institutional, social, and cultural conditions present in the country.networks, knowledge, entrepreneurs, software, China, Russia.

CATHEDRAL: A Fast and Effective Algorithm to Predict Folds and Domain Boundaries from Multidomain Protein Structures

We present CATHEDRAL, an iterative protocol for determining the location of previously observed protein folds in novel multidomain protein structures. CATHEDRAL builds on the features of a fast secondary-structure–based method (using graph theory) to locate known folds within a multidomain context and a residue-based, double-dynamic programming algorithm, which is used to align members of the target fold groups against the query protein structure to identify the closest relative and assign domain boundaries. To increase the fidelity of the assignments, a support vector machine is used to provide an optimal scoring scheme. Once a domain is verified, it is excised, and the search protocol is repeated in an iterative fashion until all recognisable domains have been identified. We have performed an initial benchmark of CATHEDRAL against other publicly available structure comparison methods using a consensus dataset of domains derived from the CATH and SCOP domain classifications. CATHEDRAL shows superior performance in fold recognition and alignment accuracy when compared with many equivalent methods. If a novel multidomain structure contains a known fold, CATHEDRAL will locate it in 90% of cases, with <1% false positives. For nearly 80% of assigned domains in a manually validated test set, the boundaries were correctly delineated within a tolerance of ten residues. For the remaining cases, previously classified domains were very remotely related to the query chain so that embellishments to the core of the fold caused significant differences in domain sizes and manual refinement of the boundaries was necessary. To put this performance in context, a well-established sequence method based on hidden Markov models was only able to detect 65% of domains, with 33% of the subsequent boundaries assigned within ten residues. Since, on average, 50% of newly determined protein structures contain more than one domain unit, and typically 90% or more of these domains are already classified in CATH, CATHEDRAL will considerably facilitate the automation of protein structure classification

Social network analysis:Foundations and frontiers on advantage

We provide an overview of social network analysis focusing on network advantage as a lens that touches on much of the area. For reasons of good data and abundant research, we draw heavily on studies of people in organizations. Advantage is traced to network structure as a proxy for the distribution of variably sticky information in a population. The network around a person indicates the person's access and control in the distribution. Advantage is a function of information breadth, timing, and arbitrage. Advantage is manifest in higher odds of proposing good ideas, more positive evaluations and recognition, higher compensation, and faster promotions. We discuss frontiers of advantage contingent on personality, cognition, embeddedness, and dynamics.</p

Accurate variational electronic structure calculations with the density matrix renormalization group

During the past 15 years, the density matrix renormalization group (DMRG) has become increasingly important for ab initio quantum chemistry. The underlying matrix product state (MPS) ansatz is a low-rank decomposition of the full configuration interaction tensor. The virtual dimension of the MPS controls the size of the corner of the many-body Hilbert space that can be reached. Whereas the MPS ansatz will only yield an efficient description for noncritical one-dimensional systems, it can still be used as a variational ansatz for other finite-size systems. Rather large virtual dimensions are then required. The two most important aspects to reduce the corresponding computational cost are a proper choice and ordering of the active space orbitals, and the exploitation of the symmetry group of the Hamiltonian. By taking care of both aspects, DMRG becomes an efficient replacement for exact diagonalization in quantum chemistry. DMRG and Hartree-Fock theory have an analogous structure. The former can be interpreted as a self-consistent mean-field theory in the DMRG lattice sites, and the latter in the particles. It is possible to build upon this analogy to introduce post-DMRG methods. Based on an approximate MPS, these methods provide improved ans\"atze for the ground state, as well as for excitations. Exponentiation of the single-particle (single-site) excitations for a Slater determinant (an MPS with open boundary conditions) leads to the Thouless theorem for Hartree-Fock theory (DMRG), an explicit nonredundant parameterization of the entire manifold of Slater determinants (MPS wavefunctions). This gives rise to the configuration interaction expansion for DMRG. The Hubbard-Stratonovich transformation lies at the basis of auxiliary field quantum Monte Carlo for Slater determinants. An analogous transformation for spin-lattice Hamiltonians allows to formulate a promising variant for MPSs.Comment: PhD thesis (225 pages). PhD thesis, Ghent University (2014), ISBN 978946197194

Shareholder networks of university spinoff companies:Firm development and regional characteristics

This paper contributes to the understanding of university spinoff (USO) development by analysing structural properties of their shareholder networks over time and across different regions. Theoretically, we propose a new stage-based typology of USO development across regions. Empirically, the study utilises a sample of 1033 academic spinoffs founded by 87 universities across 12 unitary regions in the UK considering the diversity of spatial contexts in the USO development. We undertake a social network analysis of relations USOs form with their parent universities and shareholders by adopting ‘betweenness centrality’ and ‘structural holes’ as two key measures. By employing this novel network-based view of firm development across regions, this study builds on the development model of USOs by identifying three key phases of USO development: (1) organisation phase, (2) exploitation phase, and (3) maturity and reorganisation phase. Second, we observe differences in USOs in terms of shareholder network development across diverse regional contexts. We propose a novel typology of entrepreneurial regions to better understand the diverse spatiality of USOs: peripheral lock-in, entrepreneurial periphery, rigid core, and entrepreneurial core. We call for further research to capture the long-term development and variable growth paths of USOs