A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network.

Despite various measures across different engineering and social systems, network robustness remains crucial for resisting random faults and malicious attacks. In this study, robustness refers to the ability of a network to maintain its functionality after a part of the network has failed. Existing methods assess network robustness using attack simulations, spectral measures, or deep neural networks (DNNs), which return a single metric as a result.

CNN-HT: A Two-Stage Algorithm Selection Framework.

The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework.

Influence of Removing Leaf Node Neighbors on Network Controllability.

From the perspective of network attackers, finding attack sequences that can cause significant damage to network controllability is an important task, which also helps defenders improve robustness during network constructions. Therefore, developing effective attack strategies is a key aspect of research on network controllability and its robustness. In this paper, we propose a Leaf Node Neighbor-based Attack (LNNA) strategy that can effectively disrupt the controllability of undirected networks.

Network Robustness Prediction: Influence of Training Data Distributions.

Network robustness refers to the ability of a network to continue its functioning against malicious attacks, which is critical for various natural and industrial networks. Network robustness can be quantitatively measured by a sequence of values that record the remaining functionality after a sequential node- or edge-removal attacks. Robustness evaluations are traditionally determined by attack simulations, which are computationally very time-consuming and sometimes practically infeasible.

Magnetically induced pattern formation in phase separating polymer-solvent-nanoparticle mixtures.

Permanent magnetic structures with controlled dimension and architecture (labyrinthine, hexagonal, or dispersed columnar) are formed in a partially miscible ferrofluid-nonferrofluid mixture under the influence of a perpendicular magnetic field. The origin of the permanent structures, which have characteristic lateral dimensions ranging from 1 to 10  μm, is the repartitioning of the ferrofluid carrier solvent into the nonferrofluid polymeric phase. This polymer-solvent phase separation under a magnetic field leads to departures from the expected final dimension of the magnetically stabilized ferrofluid droplet sizes.