• Corpus ID: 249191817

Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection from Dynamics

@inproceedings{Wu2022NetworkCK,
  title={Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection from Dynamics},
  author={Kai Wu and Chao Wang and Junyuan Chen and J. Liu},
  year={2022}
}
—This paper focuses on jointly inferring network and community structures from the dynamics of complex systems. Although many approaches have been designed to solve these two problems solely, none of them consider explicit shareable knowledge across these two tasks. The community detection (CD) from dynamics and network reconstruction (NR) from dynamics are natural synergistic tasks that motivate the proposed evolutionary multitasking NR and CD (EMTNRCD) framework. In the process of EMTNRCD… 

References

SHOWING 1-10 OF 70 REFERENCES

Multifactorial Evolution: Toward Evolutionary Multitasking

TLDR
This paper formalizes the concept of evolutionary multitasking and proposes an algorithm to handle multiple optimization problems simultaneously using a single population of evolving individuals and develops a cross-domain optimization platform that allows one to solve diverse problems concurrently.

A fast and elitist multiobjective genetic algorithm: NSGA-II

TLDR
This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.

An Evolutionary Multiobjective Framework for Complex Network Reconstruction Using Community Structure

TLDR
A community-based evolutionary multiobjective network reconstruction framework to promote the reconstruction performance of EA-based network reconstruction methods due to their good performance is developed, referred to as CEMO-NR, which employs the community structure of networks to divide the original decision space into multiple small decision spaces.

Evolutionary Community Detection in Dynamic Social Networks

TLDR
This work proposes a novel migration operator to work in tandem with classic genetic operators to improve the discovery of evolving community structures in dynamic social networks and presents a new method of calculating modularity directly from a genome matrix as the objective for measuring the snapshot quality.

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

TLDR
An evolutionary algorithm for solving large-scale sparse MOPs that suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions.

Network Reconstruction Based on Evolutionary-Game Data via Compressive Sensing

TLDR
Based on compressive sensing, an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data is developed, demonstrating that the extremely challenging problem of reverse engineering of complex networks can be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.

SPEA2: Improving the strength pareto evolutionary algorithm

TLDR
An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method.

Half a Dozen Real-World Applications of Evolutionary Multitasking, and More

TLDR
A set of recipes is provided showing how problem formulations of general interest, those that cut across different disciplines, could be transformed in the new light of EMT, and are intended to spark future research towards crafting novel algorithms for real-world deployment.

Evolutionary Multitasking Multilayer Network Reconstruction.

TLDR
An evolutionary multitasking multilayer network reconstruction framework to make use of the correlations among different component layers to improve the reconstruction performance is developed; this framework is referred to as EM2MNR.

Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm

...