Please join us for our next DM-Meeting.
===DM-Meeting Fall 2016===
WHO: Xinfeng Xu
WHEN: Tuesday, Nov 29, 2016 @ 12:15 noon
WHERE: Torg 3160 A
WHAT: Xinfeng will talk about the following three papers.
1. *On the Connectivity of Multi-layered Networks: Models, Measures and
Optimal Control, ICDM'15*
Networks appear naturally in many high-impact real-world applications.
In an increasingly connected and coupled world, the networks arising from
many application domains are often collected from different channels,
forming the socalled multi-layered networks, such as cyber-physical
systems, organization-level collaboration platforms, critical
infrastructure networks and many more. Compared with single-layered
networks, multi-layered networks are more vulnerable as even a small
disturbance on one supporting layer/network might cause a ripple effect to
all the dependent layers, leading to a catastrophic/cascading failure of
the entire system. The state-of-theart has been largely focusing on
modeling and manipulating the cascading effect of two-layered
interdependent network systems for some specific type of network
connectivity measure. This paper generalizes the challenge to multiple
dimensions. First, we propose a new data model for multi-layered networks
(MULAN), which admits an arbitrary number of layers with a much more
flexible dependency structure among different layers, beyond the current
pair-wise dependency. Second, we unify a wide range of classic network
connectivity measures (SUBLINE). Third, we show that for any connectivity
measure in the SUBLINE family, it enjoys the diminishing returns property
which in turn lends itself to a family of provable near-optimal control
algorithms with linear complexity. Finally, we conduct extensive empirical
evaluations on real network data, to validate the effectiveness of the
proposed algorithms.
2.* Cascading failures in interdependent networks with finite functional
components, Physical Review 2016*
We present a cascading failure model of two interdependent networks in
which functional nodes belong to components of size greater than or equal
to s. We find theoretically and via simulation that in complex networks
with random dependency links the transition is first order for s 3 and
continuous for s = 2. We also study interdependent lattices with a distance
constraint r in the dependency links and find that increasing r moves the
system from a regime without a phase transition to one with a second-order
transition. As r continues to increase, the system collapses in a
first-order transition. Each regime is associated with a different
structure of domain formation of functional nodes.
3. *Catastrophic cascade of failures in interdependent networks, Nature
2010*
Complex networks have been studied intensively for a decade, but
research still focuses on the limited case of a single, non-interacting
network1–14. Modern systems are coupled together15–19 and therefore should
be modelled as interdependent networks. A fundamental property of
interdependent networks is that failure of nodes in one network may lead to
failure of dependent nodes in other networks. This may happen recursively
and can lead to a cascade of failures. In fact, a failure of a very small
fraction of nodes in one network may lead to the complete fragmentation of
a system of several interdependent networks. A dramatic real-world example
of a cascade of failures (‘concurrent malfunction’) is the electrical
blackout that affected much of Italy on 28 September 2003: the shutdown of
power stations directly led to the failure of nodes in the Internet
communication network, which in turn caused further breakdown of power
stations20. Here we develop a framework for understanding the robustness of
interacting networks subject to such cascading failures. We present exact
analytical solutions for the critical fraction of nodes that, on removal,
will lead to a failure cascade and to a complete fragmentation of two
interdependent networks. Surprisingly, a broader degree distribution
increases the vulnerability of interdependent networks to random failure,
which is opposite to how a single network behaves. Our findings highlight
the need to consider interdependent network properties in designing robust
networks.
Regards,
Liangzhe