847 research outputs found
Non-Conservative Diffusion and its Application to Social Network Analysis
The random walk is fundamental to modeling dynamic processes on networks.
Metrics based on the random walk have been used in many applications from image
processing to Web page ranking. However, how appropriate are random walks to
modeling and analyzing social networks? We argue that unlike a random walk,
which conserves the quantity diffusing on a network, many interesting social
phenomena, such as the spread of information or disease on a social network,
are fundamentally non-conservative. When an individual infects her neighbor
with a virus, the total amount of infection increases. We classify diffusion
processes as conservative and non-conservative and show how these differences
impact the choice of metrics used for network analysis, as well as our
understanding of network structure and behavior. We show that Alpha-Centrality,
which mathematically describes non-conservative diffusion, leads to new
insights into the behavior of spreading processes on networks. We give a
scalable approximate algorithm for computing the Alpha-Centrality in a massive
graph. We validate our approach on real-world online social networks of Digg.
We show that a non-conservative metric, such as Alpha-Centrality, produces
better agreement with empirical measure of influence than conservative metrics,
such as PageRank. We hope that our investigation will inspire further
exploration into the realms of conservative and non-conservative metrics in
social network analysis
A measure of centrality based on the spectrum of the Laplacian
We introduce a family of new centralities, the k-spectral centralities.
k-Spectral centrality is a measurement of importance with respect to the
deformation of the graph Laplacian associated with the graph. Due to this
connection, k-spectral centralities have various interpretations in terms of
spectrally determined information.
We explore this centrality in the context of several examples. While for
sparse unweighted networks 1-spectral centrality behaves similarly to other
standard centralities, for dense weighted networks they show different
properties. In summary, the k-spectral centralities provide a novel and useful
measurement of relevance (for single network elements as well as whole
subnetworks) distinct from other known measures.Comment: 12 pages, 6 figures, 2 table
A similarity-based community detection method with multiple prototype representation
Communities are of great importance for understanding graph structures in
social networks. Some existing community detection algorithms use a single
prototype to represent each group. In real applications, this may not
adequately model the different types of communities and hence limits the
clustering performance on social networks. To address this problem, a
Similarity-based Multi-Prototype (SMP) community detection approach is proposed
in this paper. In SMP, vertices in each community carry various weights to
describe their degree of representativeness. This mechanism enables each
community to be represented by more than one node. The centrality of nodes is
used to calculate prototype weights, while similarity is utilized to guide us
to partitioning the graph. Experimental results on computer generated and
real-world networks clearly show that SMP performs well for detecting
communities. Moreover, the method could provide richer information for the
inner structure of the detected communities with the help of prototype weights
compared with the existing community detection models
Network Analysis with the Enron Email Corpus
We use the Enron email corpus to study relationships in a network by applying
six different measures of centrality. Our results came out of an in-semester
undergraduate research seminar. The Enron corpus is well suited to statistical
analyses at all levels of undergraduate education. Through this note's focus on
centrality, students can explore the dependence of statistical models on
initial assumptions and the interplay between centrality measures and
hierarchical ranking, and they can use completed studies as springboards for
future research. The Enron corpus also presents opportunities for research into
many other areas of analysis, including social networks, clustering, and
natural language processing.Comment: in Journal of Statistics Education, Volume 23, Number 2, 201
Correlation between centrality metrics and their application to the opinion model
In recent decades, a number of centrality metrics describing network
properties of nodes have been proposed to rank the importance of nodes. In
order to understand the correlations between centrality metrics and to
approximate a high-complexity centrality metric by a strongly correlated
low-complexity metric, we first study the correlation between centrality
metrics in terms of their Pearson correlation coefficient and their similarity
in ranking of nodes. In addition to considering the widely used centrality
metrics, we introduce a new centrality measure, the degree mass. The m order
degree mass of a node is the sum of the weighted degree of the node and its
neighbors no further than m hops away. We find that the B_{n}, the closeness,
and the components of x_{1} are strongly correlated with the degree, the
1st-order degree mass and the 2nd-order degree mass, respectively, in both
network models and real-world networks. We then theoretically prove that the
Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is
larger than that between x_{1} and a lower order degree mass. Finally, we
investigate the effect of the inflexible antagonists selected based on
different centrality metrics in helping one opinion to compete with another in
the inflexible antagonists opinion model. Interestingly, we find that selecting
the inflexible antagonists based on the leverage, the B_{n}, or the degree is
more effective in opinion-competition than using other centrality metrics in
all types of networks. This observation is supported by our previous
observations, i.e., that there is a strong linear correlation between the
degree and the B_{n}, as well as a high centrality similarity between the
leverage and the degree.Comment: 20 page
Controllability Metrics, Limitations and Algorithms for Complex Networks
This paper studies the problem of controlling complex networks, that is, the
joint problem of selecting a set of control nodes and of designing a control
input to steer a network to a target state. For this problem (i) we propose a
metric to quantify the difficulty of the control problem as a function of the
required control energy, (ii) we derive bounds based on the system dynamics
(network topology and weights) to characterize the tradeoff between the control
energy and the number of control nodes, and (iii) we propose an open-loop
control strategy with performance guarantees. In our strategy we select control
nodes by relying on network partitioning, and we design the control input by
leveraging optimal and distributed control techniques. Our findings show
several control limitations and properties. For instance, for Schur stable and
symmetric networks: (i) if the number of control nodes is constant, then the
control energy increases exponentially with the number of network nodes, (ii)
if the number of control nodes is a fixed fraction of the network nodes, then
certain networks can be controlled with constant energy independently of the
network dimension, and (iii) clustered networks may be easier to control
because, for sufficiently many control nodes, the control energy depends only
on the controllability properties of the clusters and on their coupling
strength. We validate our results with examples from power networks, social
networks, and epidemics spreading
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