212,368 research outputs found
Large-scale structure of time evolving citation networks
In this paper we examine a number of methods for probing and understanding
the large-scale structure of networks that evolve over time. We focus in
particular on citation networks, networks of references between documents such
as papers, patents, or court cases. We describe three different methods of
analysis, one based on an expectation-maximization algorithm, one based on
modularity optimization, and one based on eigenvector centrality. Using the
network of citations between opinions of the United States Supreme Court as an
example, we demonstrate how each of these methods can reveal significant
structural divisions in the network, and how, ultimately, the combination of
all three can help us develop a coherent overall picture of the network's
shape.Comment: 10 pages, 6 figures; journal names for 4 references fixe
Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vector
representation for each node, which encodes network structure and/or node
content information, such that off-the-shelf machine learning algorithms can be
easily applied to the vector-format node representations for network analysis.
However, the learned dense vector representations are inefficient for
large-scale similarity search, which requires to find the nearest neighbor
measured by Euclidean distance in a continuous vector space. In this paper, we
propose a search efficient binary network embedding algorithm called BinaryNE
to learn a sparse binary code for each node, by simultaneously modeling node
context relations and node attribute relations through a three-layer neural
network. BinaryNE learns binary node representations efficiently through a
stochastic gradient descent based online learning algorithm. The learned binary
encoding not only reduces memory usage to represent each node, but also allows
fast bit-wise comparisons to support much quicker network node search compared
to Euclidean distance or other distance measures. Our experiments and
comparisons show that BinaryNE not only delivers more than 23 times faster
search speed, but also provides comparable or better search quality than
traditional continuous vector based network embedding methods
Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network
The network of patents connected by citations is an evolving graph, which
provides a representation of the innovation process. A patent citing another
implies that the cited patent reflects a piece of previously existing knowledge
that the citing patent builds upon. A methodology presented here (i) identifies
actual clusters of patents: i.e. technological branches, and (ii) gives
predictions about the temporal changes of the structure of the clusters. A
predictor, called the {citation vector}, is defined for characterizing
technological development to show how a patent cited by other patents belongs
to various industrial fields. The clustering technique adopted is able to
detect the new emerging recombinations, and predicts emerging new technology
clusters. The predictive ability of our new method is illustrated on the
example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of
patents is determined based on citation data up to 1991, which shows
significant overlap of the class 442 formed at the beginning of 1997. These new
tools of predictive analytics could support policy decision making processes in
science and technology, and help formulate recommendations for action
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
Development of Computer Science Disciplines - A Social Network Analysis Approach
In contrast to many other scientific disciplines, computer science considers
conference publications. Conferences have the advantage of providing fast
publication of papers and of bringing researchers together to present and
discuss the paper with peers. Previous work on knowledge mapping focused on the
map of all sciences or a particular domain based on ISI published JCR (Journal
Citation Report). Although this data covers most of important journals, it
lacks computer science conference and workshop proceedings. That results in an
imprecise and incomplete analysis of the computer science knowledge. This paper
presents an analysis on the computer science knowledge network constructed from
all types of publications, aiming at providing a complete view of computer
science research. Based on the combination of two important digital libraries
(DBLP and CiteSeerX), we study the knowledge network created at
journal/conference level using citation linkage, to identify the development of
sub-disciplines. We investigate the collaborative and citation behavior of
journals/conferences by analyzing the properties of their co-authorship and
citation subgraphs. The paper draws several important conclusions. First,
conferences constitute social structures that shape the computer science
knowledge. Second, computer science is becoming more interdisciplinary. Third,
experts are the key success factor for sustainability of journals/conferences
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