5,414 research outputs found
Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks
Nowadays, identification and detection community structures in complex
networks is an important factor in extracting useful information from networks.
Label propagation algorithm with near linear-time complexity is one of the most
popular methods for detecting community structures, yet its uncertainty and
randomness is a defective factor. Merging LPA with other community detection
metrics would improve its accuracy and reduce instability of LPA. Considering
this point, in this paper we tried to use edge betweenness centrality to
improve LPA performance. On the other hand, calculating edge betweenness
centrality is expensive, so as an alternative metric, we try to use local edge
betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge
Betweenness). Experimental results on both real-world and benchmark networks
show that LPA-LEB possesses higher accuracy and stability than LPA when
detecting community structures in networks.Comment: 6 page
The Influence of Network Topology on Sound Propagation in Granular Materials
Granular materials, whose features range from the particle scale to the
force-chain scale to the bulk scale, are usually modeled as either particulate
or continuum materials. In contrast with either of these approaches, network
representations are natural for the simultaneous examination of microscopic,
mesoscopic, and macroscopic features. In this paper, we treat granular
materials as spatially-embedded networks in which the nodes (particles) are
connected by weighted edges obtained from contact forces. We test a variety of
network measures for their utility in helping to describe sound propagation in
granular networks and find that network diagnostics can be used to probe
particle-, curve-, domain-, and system-scale structures in granular media. In
particular, diagnostics of meso-scale network structure are reproducible across
experiments, are correlated with sound propagation in this medium, and can be
used to identify potentially interesting size scales. We also demonstrate that
the sensitivity of network diagnostics depends on the phase of sound
propagation. In the injection phase, the signal propagates systemically, as
indicated by correlations with the network diagnostic of global efficiency. In
the scattering phase, however, the signal is better predicted by meso-scale
community structure, suggesting that the acoustic signal scatters over local
geographic neighborhoods. Collectively, our results demonstrate how the force
network of a granular system is imprinted on transmitted waves.Comment: 19 pages, 9 figures, and 3 table
Benchmarking Measures of Network Influence
Identifying key agents for the transmission of diseases (ideas, technology,
etc.) across social networks has predominantly relied on measures of centrality
on a static base network or a temporally flattened graph of agent interactions.
Various measures have been proposed as the best trackers of influence, such as
degree centrality, betweenness, and -shell, depending on the structure of
the connectivity. We consider SIR and SIS propagation dynamics on a
temporally-extruded network of observed interactions and measure the
conditional marginal spread as the change in the magnitude of the infection
given the removal of each agent at each time: its temporal knockout (TKO)
score. We argue that the exhaustive approach of the TKO score makes it an
effective benchmark measure for evaluating the accuracy of other, often more
practical, measures of influence. We find that none of the common network
measures applied to the induced flat graphs are accurate predictors of network
propagation influence on the systems studied; however, temporal networks and
the TKO measure provide the requisite targets for the hunt for effective
predictive measures
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