479 research outputs found
Ranking and clustering of nodes in networks with smart teleportation
Random teleportation is a necessary evil for ranking and clustering directed
networks based on random walks. Teleportation enables ergodic solutions, but
the solutions must necessarily depend on the exact implementation and
parametrization of the teleportation. For example, in the commonly used
PageRank algorithm, the teleportation rate must trade off a heavily biased
solution with a uniform solution. Here we show that teleportation to links
rather than nodes enables a much smoother trade-off and effectively more robust
results. We also show that, by not recording the teleportation steps of the
random walker, we can further reduce the effect of teleportation with dramatic
effects on clustering.Comment: 10 pages, 7 figure
Coexistence of opposite opinions in a network with communities
The Majority Rule is applied to a topology that consists of two coupled
random networks, thereby mimicking the modular structure observed in social
networks. We calculate analytically the asymptotic behaviour of the model and
derive a phase diagram that depends on the frequency of random opinion flips
and on the inter-connectivity between the two communities. It is shown that
three regimes may take place: a disordered regime, where no collective
phenomena takes place; a symmetric regime, where the nodes in both communities
reach the same average opinion; an asymmetric regime, where the nodes in each
community reach an opposite average opinion. The transition from the asymmetric
regime to the symmetric regime is shown to be discontinuous.Comment: 14 pages, 4 figure
Map equation for link community
Community structure exists in many real-world networks and has been reported
being related to several functional properties of the networks. The
conventional approach was partitioning nodes into communities, while some
recent studies start partitioning links instead of nodes to find overlapping
communities of nodes efficiently. We extended the map equation method, which
was originally developed for node communities, to find link communities in
networks. This method is tested on various kinds of networks and compared with
the metadata of the networks, and the results show that our method can identify
the overlapping role of nodes effectively. The advantage of this method is that
the node community scheme and link community scheme can be compared
quantitatively by measuring the unknown information left in the networks
besides the community structure. It can be used to decide quantitatively
whether or not the link community scheme should be used instead of the node
community scheme. Furthermore, this method can be easily extended to the
directed and weighted networks since it is based on the random walk.Comment: 9 pages,5 figure
Organizational Chart Inference
Nowadays, to facilitate the communication and cooperation among employees, a
new family of online social networks has been adopted in many companies, which
are called the "enterprise social networks" (ESNs). ESNs can provide employees
with various professional services to help them deal with daily work issues.
Meanwhile, employees in companies are usually organized into different
hierarchies according to the relative ranks of their positions. The company
internal management structure can be outlined with the organizational chart
visually, which is normally confidential to the public out of the privacy and
security concerns. In this paper, we want to study the IOC (Inference of
Organizational Chart) problem to identify company internal organizational chart
based on the heterogeneous online ESN launched in it. IOC is very challenging
to address as, to guarantee smooth operations, the internal organizational
charts of companies need to meet certain structural requirements (about its
depth and width). To solve the IOC problem, a novel unsupervised method Create
(ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1)
social stratification of ESN users into different social classes, (2)
supervision link inference from managers to subordinates, and (3) consecutive
social classes matching to prune the redundant supervision links. Extensive
experiments conducted on real-world online ESN dataset demonstrate that Create
can perform very well in addressing the IOC problem.Comment: 10 pages, 9 figures, 1 table. The paper is accepted by KDD 201
Power-law distributions in empirical data
Power-law distributions occur in many situations of scientific interest and
have significant consequences for our understanding of natural and man-made
phenomena. Unfortunately, the detection and characterization of power laws is
complicated by the large fluctuations that occur in the tail of the
distribution -- the part of the distribution representing large but rare events
-- and by the difficulty of identifying the range over which power-law behavior
holds. Commonly used methods for analyzing power-law data, such as
least-squares fitting, can produce substantially inaccurate estimates of
parameters for power-law distributions, and even in cases where such methods
return accurate answers they are still unsatisfactory because they give no
indication of whether the data obey a power law at all. Here we present a
principled statistical framework for discerning and quantifying power-law
behavior in empirical data. Our approach combines maximum-likelihood fitting
methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic
and likelihood ratios. We evaluate the effectiveness of the approach with tests
on synthetic data and give critical comparisons to previous approaches. We also
apply the proposed methods to twenty-four real-world data sets from a range of
different disciplines, each of which has been conjectured to follow a power-law
distribution. In some cases we find these conjectures to be consistent with the
data while in others the power law is ruled out.Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at
http://www.santafe.edu/~aaronc/powerlaws
Bose-Einstein condensation in complex networks
The evolution of many complex systems, including the world wide web, business
and citation networks is encoded in the dynamic web describing the interactions
between the system's constituents. Despite their irreversible and
non-equilibrium nature these networks follow Bose statistics and can undergo
Bose-Einstein condensation. Addressing the dynamical properties of these
non-equilibrium systems within the framework of equilibrium quantum gases
predicts that the 'first-mover-advantage', 'fit-get-rich' and
'winner-takes-all' phenomena observed in competitive systems are
thermodynamically distinct phases of the underlying evolving networks
Stochastic blockmodels and community structure in networks
Stochastic blockmodels have been proposed as a tool for detecting community
structure in networks as well as for generating synthetic networks for use as
benchmarks. Most blockmodels, however, ignore variation in vertex degree,
making them unsuitable for applications to real-world networks, which typically
display broad degree distributions that can significantly distort the results.
Here we demonstrate how the generalization of blockmodels to incorporate this
missing element leads to an improved objective function for community detection
in complex networks. We also propose a heuristic algorithm for community
detection using this objective function or its non-degree-corrected counterpart
and show that the degree-corrected version dramatically outperforms the
uncorrected one in both real-world and synthetic networks.Comment: 11 pages, 3 figure
Assortative mixing in networks
A network is said to show assortative mixing if the nodes in the network that
have many connections tend to be connected to other nodes with many
connections. We define a measure of assortative mixing for networks and use it
to show that social networks are often assortatively mixed, but that
technological and biological networks tend to be disassortative. We propose a
model of an assortative network, which we study both analytically and
numerically. Within the framework of this model we find that assortative
networks tend to percolate more easily than their disassortative counterparts
and that they are also more robust to vertex removal.Comment: 5 pages, 1 table, 1 figur
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
Quickly routing searches without having to move content
Abstract. A great deal of work has been done to improve peer-to-peer routing by strategically moving or replicating content. However, there are many applications for which a peer-to-peer architecture might be appropriate, but in which content movement is not feasible. We argue that even in such applications, progress can be made in developing techniques that ensure efficient searches. We present several such techniques. First, we show that organizing the network into a square-root topology, where peer degrees are proportional to the square root of the popularity of their content, provides much better performance than power-law networks. Second, we present routing optimizations based on the amount of content stored at peers, and tracking the “best ” peers, that can further improve performance. These and other techniques can make searches efficient, even when content movement or replication is not feasible.
- …