25,868 research outputs found
Exposing Multi-Relational Networks to Single-Relational Network Analysis Algorithms
Many, if not most network analysis algorithms have been designed specifically
for single-relational networks; that is, networks in which all edges are of the
same type. For example, edges may either represent "friendship," "kinship," or
"collaboration," but not all of them together. In contrast, a multi-relational
network is a network with a heterogeneous set of edge labels which can
represent relationships of various types in a single data structure. While
multi-relational networks are more expressive in terms of the variety of
relationships they can capture, there is a need for a general framework for
transferring the many single-relational network analysis algorithms to the
multi-relational domain. It is not sufficient to execute a single-relational
network analysis algorithm on a multi-relational network by simply ignoring
edge labels. This article presents an algebra for mapping multi-relational
networks to single-relational networks, thereby exposing them to
single-relational network analysis algorithms.Comment: ISSN:1751-157
An Algorithm to Determine Peer-Reviewers
The peer-review process is the most widely accepted certification mechanism
for officially accepting the written results of researchers within the
scientific community. An essential component of peer-review is the
identification of competent referees to review a submitted manuscript. This
article presents an algorithm to automatically determine the most appropriate
reviewers for a manuscript by way of a co-authorship network data structure and
a relative-rank particle-swarm algorithm. This approach is novel in that it is
not limited to a pre-selected set of referees, is computationally efficient,
requires no human-intervention, and, in some instances, can automatically
identify conflict of interest situations. A useful application of this
algorithm would be to open commentary peer-review systems because it provides a
weighting for each referee with respects to their expertise in the domain of a
manuscript. The algorithm is validated using referee bid data from the 2005
Joint Conference on Digital Libraries.Comment: Rodriguez, M.A., Bollen, J., "An Algorithm to Determine
Peer-Reviewers", Conference on Information and Knowledge Management, in
press, ACM, LA-UR-06-2261, October 2008; ISBN:978-1-59593-991-
Measuring academic influence: Not all citations are equal
The importance of a research article is routinely measured by counting how
many times it has been cited. However, treating all citations with equal weight
ignores the wide variety of functions that citations perform. We want to
automatically identify the subset of references in a bibliography that have a
central academic influence on the citing paper. For this purpose, we examine
the effectiveness of a variety of features for determining the academic
influence of a citation. By asking authors to identify the key references in
their own work, we created a data set in which citations were labeled according
to their academic influence. Using automatic feature selection with supervised
machine learning, we found a model for predicting academic influence that
achieves good performance on this data set using only four features. The best
features, among those we evaluated, were those based on the number of times a
reference is mentioned in the body of a citing paper. The performance of these
features inspired us to design an influence-primed h-index (the hip-index).
Unlike the conventional h-index, it weights citations by how many times a
reference is mentioned. According to our experiments, the hip-index is a better
indicator of researcher performance than the conventional h-index
From Artifacts to Aggregations: Modeling Scientific Life Cycles on the Semantic Web
In the process of scientific research, many information objects are
generated, all of which may remain valuable indefinitely. However, artifacts
such as instrument data and associated calibration information may have little
value in isolation; their meaning is derived from their relationships to each
other. Individual artifacts are best represented as components of a life cycle
that is specific to a scientific research domain or project. Current cataloging
practices do not describe objects at a sufficient level of granularity nor do
they offer the globally persistent identifiers necessary to discover and manage
scholarly products with World Wide Web standards. The Open Archives
Initiative's Object Reuse and Exchange data model (OAI-ORE) meets these
requirements. We demonstrate a conceptual implementation of OAI-ORE to
represent the scientific life cycles of embedded networked sensor applications
in seismology and environmental sciences. By establishing relationships between
publications, data, and contextual research information, we illustrate how to
obtain a richer and more realistic view of scientific practices. That view can
facilitate new forms of scientific research and learning. Our analysis is
framed by studies of scientific practices in a large, multi-disciplinary,
multi-university science and engineering research center, the Center for
Embedded Networked Sensing (CENS).Comment: 28 pages. To appear in the Journal of the American Society for
Information Science and Technology (JASIST
Grammar-Based Random Walkers in Semantic Networks
Semantic networks qualify the meaning of an edge relating any two vertices.
Determining which vertices are most "central" in a semantic network is
difficult because one relationship type may be deemed subjectively more
important than another. For this reason, research into semantic network metrics
has focused primarily on context-based rankings (i.e. user prescribed
contexts). Moreover, many of the current semantic network metrics rank semantic
associations (i.e. directed paths between two vertices) and not the vertices
themselves. This article presents a framework for calculating semantically
meaningful primary eigenvector-based metrics such as eigenvector centrality and
PageRank in semantic networks using a modified version of the random walker
model of Markov chain analysis. Random walkers, in the context of this article,
are constrained by a grammar, where the grammar is a user defined data
structure that determines the meaning of the final vertex ranking. The ideas in
this article are presented within the context of the Resource Description
Framework (RDF) of the Semantic Web initiative.Comment: First draft of manuscript originally written in November 200
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