If the Alliance Fits . . . : Innovation and Network Dynamics.

Network formation is often said to be driven by social capital considerations. A typical pattern observed in the empirical data on strategic alliances is that of small world networks: dense subgroups of firms interconnected by (few) clique-spanning ties. The typical argument is that there is social capital value both to being embedded in a dense cluster, and to bridging disconnected clusters. In this paper we develop and analyze a simple model of joint innovation where we are able to reproduce these features, based solely on the assumption that successful partnering demands some intermediate amount of similarity between the partners.

If the Alliance Fits . . . : Innovation and Network Dynamics

Network formation is often said to be driven by social capital considerations. A typical pattern observed in the empirical data on strategic alliances is that of small world networks: dense subgroups of firms interconnected by (few) clique-spanning ties. The typical argument is that there is social capital value both to being embedded in a dense cluster, and to bridging disconnected clusters. In this paper we develop and analyze a simple model of joint innovation where we are able to reproduce these features, based solely on the assumption that successful partnering demands some intermediate amount of similarity between the partners.Network formation, Strategic alliances, Knowledge portfolios, Knowledge transfer

Grundy dominating sequences on X-join product

In this paper we study the Grundy domination number on the X-join product G↩R of a graph G and a family of graphs R={Gv:v∈V(G)}. The results led us to extend the few known families of graphs where this parameter can be efficiently computed. We prove that if, for all v∈V(G), the Grundy domination number of Gv is given, and G is a power of a cycle, a power of a path, or a split graph, computing the Grundy domination number of G↩R can be done in polynomial time. In particular, our results for powers of cycles and paths are derived from a polynomial reduction to the Maximum Weight Independent Set problem on these graphs. As a consequence, we derive closed formulas to compute the Grundy domination number of the lexicographic product G∘H when G is a power of a cycle, a power of a path or a split graph, generalizing the results on cycles and paths given by Brešar et al. in 2016. Moreover, our results on the X-join product when G is a split graph also provide polynomial-time algorithms to compute the Grundy domination number for (q,q−4) graphs, partner limited graphs and extended P4-laden graphs, graph classes that are high in the hierarchy of few P4’s graphs.Fil: Nasini, Graciela Leonor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Exactas Ingeniería y Agrimensura. Escuela de Ciencias Exactas y Naturales. Departamento de Matemática; ArgentinaFil: Torres, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Exactas Ingeniería y Agrimensura. Escuela de Ciencias Exactas y Naturales. Departamento de Matemática; Argentin

Seeking social capital and expertise in a newly-formed research community: a co-author analysis

This exploratory study applies social network analysis techniques to existing, publicly available data to understand collaboration patterns within the co-author network of a federally-funded, interdisciplinary research program. The central questions asked: What underlying social capital structures can be determined about a group of researchers from bibliometric data and other publicly available existing data? What are ways social network tools characterize the interdisciplinarity or cross-disciplinarity of co-author teams? The names of 411 grantees were searched in the Web of Science indexing database; author information from the WoS search results resulted in a 191-member co-author network. Research domains were included as attribute data for the co-author network. UCINet social network analysis software calculated a large 60 node component and two larger components with 12 and 8 nodes respectively, the remainder of the network consisted of smaller 2-5 node components. Within the 191-node co-author network the following analyses were performed to learn more about the structural social capital of this group: Degree and Eigenvector centrality measures, brokerage measures, and constraint measures. Additionally, ten randomly selected dyads and the five 4-node cliques within the 191-node network were examined to find patterns of cross-disciplinary collaboration among researcher and within award teams. Award numbers were added as attribute data to five 4-node cliques and 10 random dyads; these showed instances of collaboration among interdisciplinary award teams. Collaboration patterns across disciplines are discussed. Data from this research could serve as a baseline measure for growth in future analyses of the case studied. This method is recommended as a tool to gain insights to a research community and to track publication collaboration growth over time. This research method shows potential as a way to identify aspects of a research community’s social structural capital, particularly within an interdisciplinary network to highlight where researchers are working well together or to learn where there is little collaboration

Topological Graph Neural Networks

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures, such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive in terms of the Weisfeiler--Lehman test of isomorphism. Augmenting GNNs with our layer leads to beneficial predictive performance for graph and node classification tasks, both on synthetic data sets, which can be classified by humans using their topology but not by ordinary GNNs, and on real-world data