22,170 research outputs found
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
Community detection in directed acyclic graphs
Some temporal networks, most notably citation networks, are naturally
represented as directed acyclic graphs (DAGs). To detect communities in DAGs,
we propose a modularity for DAGs by defining an appropriate null model (i.e.,
randomized network) respecting the order of nodes. We implement a spectral
method to approximately maximize the proposed modularity measure and test the
method on citation networks and other DAGs. We find that the attained values of
the modularity for DAGs are similar for partitions that we obtain by maximizing
the proposed modularity (designed for DAGs), the modularity for undirected
networks and that for general directed networks. In other words, if we neglect
the order imposed on nodes (and the direction of links) in a given DAG and
maximize the conventional modularity measure, the obtained partition is close
to the optimal one in the sense of the modularity for DAGs.Comment: 2 figures, 7 table
Network Capacity Bound for Personalized PageRank in Multimodal Networks
In a former paper the concept of Bipartite PageRank was introduced and a
theorem on the limit of authority flowing between nodes for personalized
PageRank has been generalized. In this paper we want to extend those results to
multimodal networks. In particular we introduce a hypergraph type that may be
used for describing multimodal network where a hyperlink connects nodes from
each of the modalities. We introduce a generalisation of PageRank for such
graphs and define the respective random walk model that can be used for
computations. we finally state and prove theorems on the limit of outflow of
authority for cases where individual modalities have identical and distinct
damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373
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