14,797 research outputs found
Detecting communities via edge Random Walk Centrality
Herein we present a novel approach of identifying community structures in
complex networks. We propose the usage of the Random Walk Centrality (RWC),
first introduced by Noh and Rieger [Phys. Rev. Lett. 92.11 (2004): 118701]. We
adapt this node centrality metric to an edge centrality metric by applying it
to the line graph of a given network. A crucial feature of our algorithm is the
needlessness of recalculating the centrality metric after each step, in
contrast to most community detection algorithms. We test our algorithm on a
wide variety of standard networks, and compare them with pre-existing
algorithms. As a predictive application, we analyze the Indian Railway network
for robustness and connectedness, and propose edges which would make the system
even sturdier.Comment: 12 pages, 8 figure
Novel Edge and Density Metrics for Link Cohesion
We present a new metric of link cohesion for measuring the strength of edges
in complex, highly connected graphs. Link cohesion accounts for local small hop
connections and associated node degrees and can be used to support edge scoring
and graph simplification. We also present a novel graph density measure to
estimate the average cohesion across nodes. Link cohesion and the density
measure are employed to demonstrate community detection through graph
sparsification by maximizing graph density. Link cohesion is also shown to be
loosely correlated with edge betweenness centrality
Centrality Metric for Dynamic Networks
Centrality is an important notion in network analysis and is used to measure
the degree to which network structure contributes to the importance of a node
in a network. While many different centrality measures exist, most of them
apply to static networks. Most networks, on the other hand, are dynamic in
nature, evolving over time through the addition or deletion of nodes and edges.
A popular approach to analyzing such networks represents them by a static
network that aggregates all edges observed over some time period. This
approach, however, under or overestimates centrality of some nodes. We address
this problem by introducing a novel centrality metric for dynamic network
analysis. This metric exploits an intuition that in order for one node in a
dynamic network to influence another over some period of time, there must exist
a path that connects the source and destination nodes through intermediaries at
different times. We demonstrate on an example network that the proposed metric
leads to a very different ranking than analysis of an equivalent static
network. We use dynamic centrality to study a dynamic citations network and
contrast results to those reached by static network analysis.Comment: in KDD workshop on Mining and Learning in Graphs (MLG
Offloading Content with Self-organizing Mobile Fogs
Mobile users in an urban environment access content on the internet from
different locations. It is challenging for the current service providers to
cope with the increasing content demand from a large number of collocated
mobile users. In-network caching to offload content at nodes closer to users
alleviate the issue, though efficient cache management is required to find out
who should cache what, when and where in an urban environment, given nodes
limited computing, communication and caching resources. To address this, we
first define a novel relation between content popularity and availability in
the network and investigate a node's eligibility to cache content based on its
urban reachability. We then allow nodes to self-organize into mobile fogs to
increase the distributed cache and maximize content availability in a
cost-effective manner. However, to cater rational nodes, we propose a coalition
game for the nodes to offer a maximum "virtual cache" assuming a monetary
reward is paid to them by the service/content provider. Nodes are allowed to
merge into different spatio-temporal coalitions in order to increase the
distributed cache size at the network edge. Results obtained through
simulations using realistic urban mobility trace validate the performance of
our caching system showing a ratio of 60-85% of cache hits compared to the
30-40% obtained by the existing schemes and 10% in case of no coalition
Numerical Investigation of Metrics for Epidemic Processes on Graphs
This study develops the epidemic hitting time (EHT) metric on graphs
measuring the expected time an epidemic starting at node in a fully
susceptible network takes to propagate and reach node . An associated EHT
centrality measure is then compared to degree, betweenness, spectral, and
effective resistance centrality measures through exhaustive numerical
simulations on several real-world network data-sets. We find two surprising
observations: first, EHT centrality is highly correlated with effective
resistance centrality; second, the EHT centrality measure is much more
delocalized compared to degree and spectral centrality, highlighting the role
of peripheral nodes in epidemic spreading on graphs.Comment: 6 pages, 1 figure, 3 tables, In Proceedings of 2015 Asilomar
Conference on Signals, Systems, and Computer
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