58,412 research outputs found
Search in Power-Law Networks
Many communication and social networks have power-law link distributions,
containing a few nodes which have a very high degree and many with low degree.
The high connectivity nodes play the important role of hubs in communication
and networking, a fact which can be exploited when designing efficient search
algorithms. We introduce a number of local search strategies which utilize high
degree nodes in power-law graphs and which have costs which scale sub-linearly
with the size of the graph. We also demonstrate the utility of these strategies
on the Gnutella peer-to-peer network.Comment: 17 pages, 14 figure
On the Stability of Community Detection Algorithms on Longitudinal Citation Data
There are fundamental differences between citation networks and other classes
of graphs. In particular, given that citation networks are directed and
acyclic, methods developed primarily for use with undirected social network
data may face obstacles. This is particularly true for the dynamic development
of community structure in citation networks. Namely, it is neither clear when
it is appropriate to employ existing community detection approaches nor is it
clear how to choose among existing approaches. Using simulated data, we attempt
to clarify the conditions under which one should use existing methods and which
of these algorithms is appropriate in a given context. We hope this paper will
serve as both a useful guidepost and an encouragement to those interested in
the development of more targeted approaches for use with longitudinal citation
data.Comment: 17 pages, 7 figures, presenting at Applications of Social Network
Analysis 2009, ETH Zurich Edit, August 17, 2009: updated abstract, figures,
text clarification
ES Is More Than Just a Traditional Finite-Difference Approximator
An evolution strategy (ES) variant based on a simplification of a natural
evolution strategy recently attracted attention because it performs
surprisingly well in challenging deep reinforcement learning domains. It
searches for neural network parameters by generating perturbations to the
current set of parameters, checking their performance, and moving in the
aggregate direction of higher reward. Because it resembles a traditional
finite-difference approximation of the reward gradient, it can naturally be
confused with one. However, this ES optimizes for a different gradient than
just reward: It optimizes for the average reward of the entire population,
thereby seeking parameters that are robust to perturbation. This difference can
channel ES into distinct areas of the search space relative to gradient
descent, and also consequently to networks with distinct properties. This
unique robustness-seeking property, and its consequences for optimization, are
demonstrated in several domains. They include humanoid locomotion, where
networks from policy gradient-based reinforcement learning are significantly
less robust to parameter perturbation than ES-based policies solving the same
task. While the implications of such robustness and robustness-seeking remain
open to further study, this work's main contribution is to highlight such
differences and their potential importance
Ordered community structure in networks
Community structure in networks is often a consequence of homophily, or
assortative mixing, based on some attribute of the vertices. For example,
researchers may be grouped into communities corresponding to their research
topic. This is possible if vertex attributes have discrete values, but many
networks exhibit assortative mixing by some continuous-valued attribute, such
as age or geographical location. In such cases, no discrete communities can be
identified. We consider how the notion of community structure can be
generalized to networks that are based on continuous-valued attributes: in
general, a network may contain discrete communities which are ordered according
to their attribute values. We propose a method of generating synthetic ordered
networks and investigate the effect of ordered community structure on the
spread of infectious diseases. We also show that community detection algorithms
fail to recover community structure in ordered networks, and evaluate an
alternative method using a layout algorithm to recover the ordering.Comment: This is an extended preprint version that includes an extra example:
the college football network as an ordered (spatial) network. Further
improvements, not included here, appear in the journal version. Original
title changed (from "Ordered and continuous community structure in networks")
to match journal versio
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