Titlebook: Computational Intelligence for Network Structure Analytics; Maoguo Gong,Qing Cai,Yu Lei Book 2017 Springer Nature Singapore Pte Ltd. 2017

自制

Expertise

Johan J?rgen Holst and Arms Controlst, an evolutionary multiobjective algorithm is used for recommendation. And then, a memetic algorithm for influence maximization is introduced. Finally, a memetic algorithm for global biological network alignment is presented.

needle

Network Community Discovery with Evolutionary Single-Objective Optimization,rks. This chapter focuses on evolutionary single-objective algorithms for solving network community discovery. First this chapter reviews evolutionary single-objective algorithm for network community discovery. Then three representative algorithms and their performances of discovering communities are introduced in detail.

chisel

Network Community Discovery with Evolutionary Multi-objective Optimization,ity detection based multi-objective optimization problems. Among the four algorithms, three algorithms adopt the framework of MOEA/D, MODPSO, and NNIA to detect multi-resolution communities in undirected and static networks, and an algorithm uses the framework of MOEA/D to detect multi-resolution communities in dynamic networks.

GLARE

Real-World Cases of Network Structure Analytics,st, an evolutionary multiobjective algorithm is used for recommendation. And then, a memetic algorithm for influence maximization is introduced. Finally, a memetic algorithm for global biological network alignment is presented.

ARENA

Femish

SIMUS Applied to Quantify SWOT Strategiessuch as network construction, information backbone mining, structure analytics of large-scale networks, etc. These topics can also be formulated as optimization problems and may be well solved by computational intelligence methods. In this chapter, we will give several future research directions that we are working on.

Vertical

A Comment on the Palme Commission Reportn are introduced. Next, a multilevel learning based memetic algorithm for the balance computation and the balance transformation of signed networks in a weak definition are presented. Finally, a two-step method based on evolutionary multi-objective optimization for weak structure balance are presented.

Tincture

SEVER

Network Structure Balance Analytics with Evolutionary Optimization,n are introduced. Next, a multilevel learning based memetic algorithm for the balance computation and the balance transformation of signed networks in a weak definition are presented. Finally, a two-step method based on evolutionary multi-objective optimization for weak structure balance are presented.