<![CDATA[ IEEE Transactions on Network Science and Engineering - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 6488902 2019January 21<![CDATA[A Resource Allocation Mechanism for Cloud Radio Access Network Based on Cell Differentiation and Integration Concept]]>$approx 16%$ is estimated in a CDI-enabled C-RAN when compared to a fixed C-RAN, both serving the same geographical area.]]>542612751598<![CDATA[Belief Dynamics in Social Networks: A Fluid-Based Analysis]]>54276287668<![CDATA[Cascading Edge Failures: A Dynamic Network Process]]>$(i,j)$ in a network. The probability that a working link fails or a failed link recovers may be independent of the state of other links or may be dependent locally on the state of neighboring links as described by a cascade function $f$. In applications, this means that failures or recovery of links may have a regional preference, or, alternatively, relationships between neighbors in the network can lead to changes in the links between neighbors of neighbors. This paper shows that the dynamic evolution of $P(mathbf{A},t)$, the probability that the network is in some state $mathbf{A}$, describing the collective states of all the links at time $t$, converges to a stationary distribution. We use this distribution to study the emergence of global behaviors like consensus (i.e., catastrophic failure or full recovery of all the edges) or mixed (i.e., some failed and some working substructures). In particular, we show that, depending on the local dynamical rule, different network -
ubstructures, such as hub or triangle subgraphs, are more prone to failure.]]>54288300898<![CDATA[Comparing the Effects of Failures in Power Grids Under the AC and DC Power Flow Models]]>543013121903<![CDATA[Detecting Cascades from Weak Signatures]]>54313325755<![CDATA[Minimizing Social Cost of Vaccinating Network SIS Epidemics]]>543263351008<![CDATA[Modelling Spreading Process Induced by Agent Mobility in Complex Networks]]>Susceptible-Infected-Removed (SIR) epidemic model and use continuous-time Markov chain analysis to model the impact of such agent mobility induced contagion mechanics by taking into account the state transitions of each node individually, as oppose to most conventional epidemic approaches which usually consider the mean aggregated behavior of all nodes. Our approach makes one mean field approximation to reduce complexity from exponential to polynomial. We study both network-wide properties such as epidemic threshold as well as individual node vulnerability under such agent assisted infection spreading process. Furthermore, we provide a first order approximation on the agents’ vulnerability since infection is bi-directional. We compare our analysis of spreading process induced by agent mobility against contact-based epidemic model via a case study on London Underground network, the second busiest metro system in Europe, with real dataset recording commuters’ activities in the system. We highlight the key differences in the spreading patterns between the contact-based versus agent assisted spreading models. Specifically, we show that our model predicts greater spreading radius than conventional contact-based models due to agents’ movements. Another interesting finding is that, in contrast to contact-based model where nodes located more centrally in a network are proportionally more prone to infection, our model shows no such strict correlation as in our model, nodes may not be highly susceptible even located at the heart of -
he network and vice versa.]]>543363491479