41,604 research outputs found
Predicting Multi-actor collaborations using Hypergraphs
Social networks are now ubiquitous and most of them contain interactions
involving multiple actors (groups) like author collaborations, teams or emails
in an organizations, etc. Hypergraphs are natural structures to effectively
capture multi-actor interactions which conventional dyadic graphs fail to
capture. In this work the problem of predicting collaborations is addressed
while modeling the collaboration network as a hypergraph network. The problem
of predicting future multi-actor collaboration is mapped to hyperedge
prediction problem. Given that the higher order edge prediction is an
inherently hard problem, in this work we restrict to the task of predicting
edges (collaborations) that have already been observed in past. In this work,
we propose a novel use of hyperincidence temporal tensors to capture time
varying hypergraphs and provides a tensor decomposition based prediction
algorithm. We quantitatively compare the performance of the hypergraphs based
approach with the conventional dyadic graph based approach. Our hypothesis that
hypergraphs preserve the information that simple graphs destroy is corroborated
by experiments using author collaboration network from the DBLP dataset. Our
results demonstrate the strength of hypergraph based approach to predict higher
order collaborations (size>4) which is very difficult using dyadic graph based
approach. Moreover, while predicting collaborations of size>2 hypergraphs in
most cases provide better results with an average increase of approx. 45% in
F-Score for different sizes = {3,4,5,6,7}
Investigating the impact of networking capability on firm innovation performance:using the resource-action-performance framework
The author's final peer reviewed version can be found by following the URI link. The Publisher's final version can be found by following the DOI link.Purpose
The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry.
Design/methodology/approach
Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses.
Findings
The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance.
Originality/value
Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research
Data-driven modeling of collaboration networks: A cross-domain analysis
We analyze large-scale data sets about collaborations from two different
domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and
science, specifically 300.000 co-authorship relations between 95.000
scientists. Considering the different domains of the data sets, we address two
questions: (a) to what extent do the collaboration networks reconstructed from
the data share common structural features, and (b) can their structure be
reproduced by the same agent-based model. In our data-driven modeling approach
we use aggregated network data to calibrate the probabilities at which agents
establish collaborations with either newcomers or established agents. The model
is then validated by its ability to reproduce network features not used for
calibration, including distributions of degrees, path lengths, local clustering
coefficients and sizes of disconnected components. Emphasis is put on comparing
domains, but also sub-domains (economic sectors, scientific specializations).
Interpreting the link probabilities as strategies for link formation, we find
that in R&D collaborations newcomers prefer links with established agents,
while in co-authorship relations newcomers prefer links with other newcomers.
Our results shed new light on the long-standing question about the role of
endogenous and exogenous factors (i.e., different information available to the
initiator of a collaboration) in network formation.Comment: 25 pages, 13 figures, 4 table
The Lotic Intersite Nitrogen Experiments: an example of successful ecological research collaboration
Collaboration is an essential skill for modern ecologists because it brings together diverse expertise, viewpoints, and study systems. The Lotic Intersite Nitrogen eXperiments (LINX I and II), a 17-y research endeavor involving scores of early- to late-career stream ecologists, is an example of the benefits, challenges, and approaches of successful collaborative research in ecology. The scientific success of LINX reflected tangible attributes including clear scientific goals (hypothesis-driven research), coordinated research methods, a team of cooperative scientists, excellent leadership, extensive communication, and a philosophy of respect for input from all collaborators. Intangible aspects of the collaboration included camaraderie and strong team chemistry. LINX further benefited from being part of a discipline in which collaboration is a tradition, clear data-sharing and authorship guidelines, an approach that melded field experiments and modeling, and a shared collaborative goal in the form of a universal commitment to see the project and resulting data products through to completion
Communities, Knowledge Creation, and Information Diffusion
In this paper, we examine how patterns of scientific collaboration contribute
to knowledge creation. Recent studies have shown that scientists can benefit
from their position within collaborative networks by being able to receive more
information of better quality in a timely fashion, and by presiding over
communication between collaborators. Here we focus on the tendency of
scientists to cluster into tightly-knit communities, and discuss the
implications of this tendency for scientific performance. We begin by reviewing
a new method for finding communities, and we then assess its benefits in terms
of computation time and accuracy. While communities often serve as a taxonomic
scheme to map knowledge domains, they also affect how successfully scientists
engage in the creation of new knowledge. By drawing on the longstanding debate
on the relative benefits of social cohesion and brokerage, we discuss the
conditions that facilitate collaborations among scientists within or across
communities. We show that successful scientific production occurs within
communities when scientists have cohesive collaborations with others from the
same knowledge domain, and across communities when scientists intermediate
among otherwise disconnected collaborators from different knowledge domains. We
also discuss the implications of communities for information diffusion, and
show how traditional epidemiological approaches need to be refined to take
knowledge heterogeneity into account and preserve the system's ability to
promote creative processes of novel recombinations of idea
Searching for Communities in Bipartite Networks
Bipartite networks are a useful tool for representing and investigating
interaction networks. We consider methods for identifying communities in
bipartite networks. Intuitive notions of network community groups are made
explicit using Newman's modularity measure. A specialized version of the
modularity, adapted to be appropriate for bipartite networks, is presented; a
corresponding algorithm is described for identifying community groups through
maximizing this measure. The algorithm is applied to networks derived from the
EU Framework Programs on Research and Technological Development. Community
groups identified are compared using information-theoretic methods.Comment: 12 pages, 4 figures, to appear in "Proceedings of the 5th Jagna
International Workshop: Stochastic and Quantum Dynamics of Biomolecular
Systems," C. C. Bernido and M. V. Carpio-Bernido, editors. A version with
full-quality figures and larger file size is available at
http://ccm.uma.pt/publications/Barber-Faria-Streit-Strogan-2008.pd
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