3,975 research outputs found
A Connectedness Constraint for Learning Sparse Graphs
Graphs are naturally sparse objects that are used to study many problems
involving networks, for example, distributed learning and graph signal
processing. In some cases, the graph is not given, but must be learned from the
problem and available data. Often it is desirable to learn sparse graphs.
However, making a graph highly sparse can split the graph into several
disconnected components, leading to several separate networks. The main
difficulty is that connectedness is often treated as a combinatorial property,
making it hard to enforce in e.g. convex optimization problems. In this
article, we show how connectedness of undirected graphs can be formulated as an
analytical property and can be enforced as a convex constraint. We especially
show how the constraint relates to the distributed consensus problem and graph
Laplacian learning. Using simulated and real data, we perform experiments to
learn sparse and connected graphs from data.Comment: 5 pages, presented at the European Signal Processing Conference
(EUSIPCO) 201
Functional Integration of Ecological Networks through Pathway Proliferation
Large-scale structural patterns commonly occur in network models of complex
systems including a skewed node degree distribution and small-world topology.
These patterns suggest common organizational constraints and similar functional
consequences. Here, we investigate a structural pattern termed pathway
proliferation. Previous research enumerating pathways that link species
determined that as pathway length increases, the number of pathways tends to
increase without bound. We hypothesize that this pathway proliferation
influences the flow of energy, matter, and information in ecosystems. In this
paper, we clarify the pathway proliferation concept, introduce a measure of the
node--node proliferation rate, describe factors influencing the rate, and
characterize it in 17 large empirical food-webs. During this investigation, we
uncovered a modular organization within these systems. Over half of the
food-webs were composed of one or more subgroups that were strongly connected
internally, but weakly connected to the rest of the system. Further, these
modules had distinct proliferation rates. We conclude that pathway
proliferation in ecological networks reveals subgroups of species that will be
functionally integrated through cyclic indirect effects.Comment: 29 pages, 2 figures, 3 tables, Submitted to Journal of Theoretical
Biolog
Defining and Evaluating Network Communities based on Ground-truth
Nodes in real-world networks organize into densely linked communities where
edges appear with high concentration among the members of the community.
Identifying such communities of nodes has proven to be a challenging task
mainly due to a plethora of definitions of a community, intractability of
algorithms, issues with evaluation and the lack of a reliable gold-standard
ground-truth.
In this paper we study a set of 230 large real-world social, collaboration
and information networks where nodes explicitly state their group memberships.
For example, in social networks nodes explicitly join various interest based
social groups. We use such groups to define a reliable and robust notion of
ground-truth communities. We then propose a methodology which allows us to
compare and quantitatively evaluate how different structural definitions of
network communities correspond to ground-truth communities. We choose 13
commonly used structural definitions of network communities and examine their
sensitivity, robustness and performance in identifying the ground-truth. We
show that the 13 structural definitions are heavily correlated and naturally
group into four classes. We find that two of these definitions, Conductance and
Triad-participation-ratio, consistently give the best performance in
identifying ground-truth communities. We also investigate a task of detecting
communities given a single seed node. We extend the local spectral clustering
algorithm into a heuristic parameter-free community detection method that
easily scales to networks with more than hundred million nodes. The proposed
method achieves 30% relative improvement over current local clustering methods.Comment: Proceedings of 2012 IEEE International Conference on Data Mining
(ICDM), 201
Metrics for Graph Comparison: A Practitioner's Guide
Comparison of graph structure is a ubiquitous task in data analysis and
machine learning, with diverse applications in fields such as neuroscience,
cyber security, social network analysis, and bioinformatics, among others.
Discovery and comparison of structures such as modular communities, rich clubs,
hubs, and trees in data in these fields yields insight into the generative
mechanisms and functional properties of the graph.
Often, two graphs are compared via a pairwise distance measure, with a small
distance indicating structural similarity and vice versa. Common choices
include spectral distances (also known as distances) and distances
based on node affinities. However, there has of yet been no comparative study
of the efficacy of these distance measures in discerning between common graph
topologies and different structural scales.
In this work, we compare commonly used graph metrics and distance measures,
and demonstrate their ability to discern between common topological features
found in both random graph models and empirical datasets. We put forward a
multi-scale picture of graph structure, in which the effect of global and local
structure upon the distance measures is considered. We make recommendations on
the applicability of different distance measures to empirical graph data
problem based on this multi-scale view. Finally, we introduce the Python
library NetComp which implements the graph distances used in this work
Threshold selection in gene co-expression networks using spectral graph theory techniques
Abstract Background
Gene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff. Results
We apply spectral graph theory methods to develop a systematic method for threshold selection. Eigenvalues and eigenvectors are computed for a transformation of the adjacency matrix of the network constructed at various threshold values. From these, we use a basic spectral clustering method to examine the set of gene-gene relationships and select a threshold dependent upon the community structure of the data. This approach is applied to two well-studied microarray data sets from Homo sapiens and Saccharomyces cerevisiae. Conclusion
This method presents a systematic, data-based alternative to using more artificial cutoff values and results in a more conservative approach to threshold selection than some other popular techniques such as retaining only statistically-significant relationships or setting a cutoff to include a percentage of the highest correlations
Community Detection in Quantum Complex Networks
Determining community structure is a central topic in the study of complex
networks, be it technological, social, biological or chemical, in static or
interacting systems. In this paper, we extend the concept of community
detection from classical to quantum systems---a crucial missing component of a
theory of complex networks based on quantum mechanics. We demonstrate that
certain quantum mechanical effects cannot be captured using current classical
complex network tools and provide new methods that overcome these problems. Our
approaches are based on defining closeness measures between nodes, and then
maximizing modularity with hierarchical clustering. Our closeness functions are
based on quantum transport probability and state fidelity, two important
quantities in quantum information theory. To illustrate the effectiveness of
our approach in detecting community structure in quantum systems, we provide
several examples, including a naturally occurring light-harvesting complex,
LHCII. The prediction of our simplest algorithm, semiclassical in nature,
mostly agrees with a proposed partitioning for the LHCII found in quantum
chemistry literature, whereas our fully quantum treatment of the problem
uncovers a new, consistent, and appropriately quantum community structure.Comment: 16 pages, 4 figures, 1 tabl
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