16,895 research outputs found
Generalized Network Dismantling
Finding the set of nodes, which removed or (de)activated can stop the spread
of (dis)information, contain an epidemic or disrupt the functioning of a
corrupt/criminal organization is still one of the key challenges in network
science. In this paper, we introduce the generalized network dismantling
problem, which aims to find the set of nodes that, when removed from a network,
results in a network fragmentation into subcritical network components at
minimum cost. For unit costs, our formulation becomes equivalent to the
standard network dismantling problem. Our non-unit cost generalization allows
for the inclusion of topological cost functions related to node centrality and
non-topological features such as the price, protection level or even social
value of a node. In order to solve this optimization problem, we propose a
method, which is based on the spectral properties of a novel node-weighted
Laplacian operator. The proposed method is applicable to large-scale networks
with millions of nodes. It outperforms current state-of-the-art methods and
opens new directions in understanding the vulnerability and robustness of
complex systems.Comment: 6 pages, 5 figure
Ensemble approach for generalized network dismantling
Finding a set of nodes in a network, whose removal fragments the network
below some target size at minimal cost is called network dismantling problem
and it belongs to the NP-hard computational class. In this paper, we explore
the (generalized) network dismantling problem by exploring the spectral
approximation with the variant of the power-iteration method. In particular, we
explore the network dismantling solution landscape by creating the ensemble of
possible solutions from different initial conditions and a different number of
iterations of the spectral approximation.Comment: 11 Pages, 4 Figures, 4 Table
Fast and simple decycling and dismantling of networks
Decycling and dismantling of complex networks are underlying many important
applications in network science. Recently these two closely related problems
were tackled by several heuristic algorithms, simple and considerably
sub-optimal, on the one hand, and time-consuming message-passing ones that
evaluate single-node marginal probabilities, on the other hand. In this paper
we propose a simple and extremely fast algorithm, CoreHD, which recursively
removes nodes of the highest degree from the -core of the network. CoreHD
performs much better than all existing simple algorithms. When applied on
real-world networks, it achieves equally good solutions as those obtained by
the state-of-art iterative message-passing algorithms at greatly reduced
computational cost, suggesting that CoreHD should be the algorithm of choice
for many practical purposes
Underestimated cost of targeted attacks on complex networks
The robustness of complex networks under targeted attacks is deeply connected
to the resilience of complex systems, i.e., the ability to make appropriate
responses to the attacks. In this article, we investigated the state-of-the-art
targeted node attack algorithms and demonstrate that they become very
inefficient when the cost of the attack is taken into consideration. In this
paper, we made explicit assumption that the cost of removing a node is
proportional to the number of adjacent links that are removed, i.e., higher
degree nodes have higher cost. Finally, for the case when it is possible to
attack links, we propose a simple and efficient edge removal strategy named
Hierarchical Power Iterative Normalized cut (HPI-Ncut).The results on real and
artificial networks show that the HPI-Ncut algorithm outperforms all the node
removal and link removal attack algorithms when the cost of the attack is taken
into consideration. In addition, we show that on sparse networks, the
complexity of this hierarchical power iteration edge removal algorithm is only
.Comment: 14 pages, 7 figure
Integrating Closed-loop Supply Chains and Spare Parts Management at IBM
Ever more companies are recognizing the benefits of closed-loop supplychains that integrate product returns into business operations. IBMhas been among the pioneers seeking to unlock the value dormant inthese resources. We report on a project exploiting product returns asa source of spare parts. Key decisions include the choice of recoveryopportunities to use, the channel design, and the coordination ofalternative supply sources. We developed an analytic inventory controlmodel and a simulation model to address these issues. Our results showthat procurement cost savings largely outweigh reverse logistics costsand that information management is key to an efficient solution. Ourrecommendations provide a basis for significantly expanding the usageof the novel parts supply source, which allows for cutting procurementcosts.supply chain management;reverse logistics;product recovery;inventory management;service management
Optimization of the long-term planning of supply chains with decaying performance
This master's thesis addresses the optimization of supply and distribution chains considering the effect that equipment aging may cause over the performance of facilities involved in the process. The decaying performance of the facilities is modeled as an exponential equation and can be either physical or economic, thus giving rise to a novel mixed integer non-linear programming (MINLP) formulation. The optimization model has been developed based on a typical chemical supply chain. Thus, the best long-term investment plan has to be determined given production nodes, their production capacity and expected evolution; aggregated consumption nodes (urban or industrial districts) and their lumped demand (and expected evolution); actual and potential distribution nodes; distances between the nodes of the network; and a time horizon. The model includes the balances in each node, a general decaying performance function, and a cost function, as well as constraints to be satisfied. Hence, the investment plan (decision variables) consists not only on the start-up and shutdown of alternative distribution facilities, but also on the sizing of the lines satisfying the flows. The model has been implemented using GAMS optimization software. Results considering a variety of scenarios have been discussed. In addition, different approaches to the starting point for the model have been compared, showing the importance of initializing the optimization algorithm. The capabilities of the proposed approach have been tested through its application to two case studies: a natural gas network with physical decaying performance and an electricity distribution network with economic decaying performance. Each case study is solved with a different procedure to obtain results. Results demonstrate that overlooking the effect of equipment aging can lead to infeasible (for physical decaying performance) or unrealistic (for economic decaying performance) solutions in practice and show how the proposed model allows overcoming such limitations thus becoming a practical tool to support the decision-making process in the distribution secto
Unveiling Explosive Vulnerability of Networks through Edge Collective Behavior
Edges, binding together nodes within networks, have the potential to induce
dramatic transitions when specific collective failure behaviors emerge. These
changes, initially unfolding covertly and then erupting abruptly, pose
substantial, unforeseeable threats to networked systems, and are termed
explosive vulnerability. Thus, identifying influential edges capable of
triggering such drastic transitions, while minimizing cost, is of utmost
significance. Here, we address this challenge by introducing edge collective
influence (ECI), which builds upon the optimal percolation theory applied to
line graphs. ECI embodies features of both optimal and explosive percolation,
involving minimized removal costs and explosive dismantling tactic.
Furthermore, we introduce two improved versions of ECI, namely IECI and IECIR,
tailored for objectives of hidden and fast dismantling, respectively, with
their superior performance validated in both synthetic and empirical networks.
Finally, we present a dual competitive percolation (DCP) model, whose reverse
process replicates the explosive dismantling process and the trajectory of the
cost function of ECI, elucidating the microscopic mechanisms enabling ECI's
optimization. ECI and the DCP model demonstrate the profound connection between
optimal and explosive percolation. This work significantly deepens our
comprehension of percolation and provides valuable insights into the explosive
vulnerabilities arising from edge collective behaviors.Comment: 19 pages, 11 figures, 2 table
Embedding-aided network dismantling
Optimal percolation concerns the identification of the minimum-cost strategy
for the destruction of any extensive connected components in a network.
Solutions of such a dismantling problem are important for the design of optimal
strategies of disease containment based either on immunization or social
distancing. Depending on the specific variant of the problem considered,
network dismantling is performed via the removal of nodes or edges, and
different cost functions are associated to the removal of these microscopic
elements. In this paper, we show that network representations in geometric
space can be used to solve several variants of the network dismantling problem
in a coherent fashion. Once a network is embedded, dismantling is implemented
using intuitive geometric strategies. We demonstrate that the approach well
suits both Euclidean and hyperbolic network embeddings. Our systematic analysis
on synthetic and real networks demonstrates that the performance of
embedding-aided techniques is comparable to, if not better than, the one of the
best dismantling algorithms currently available on the market.Comment: 13 pages, 5 figures, 1 table + SM available at
https://cgi.luddy.indiana.edu/~filiradi/Mypapers/SM_geo_percolation.pd
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