Titlebook: Complex Networks XV; Proceedings of the 1 Federico Botta,Mariana Macedo,Ronaldo Menezes Conference proceedings 2024 The Editor(s) (if appli

cerebral-cortex

書(shū)目名稱(chēng)Complex Networks XV影響因子(影響力)




書(shū)目名稱(chēng)Complex Networks XV影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Complex Networks XV網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Complex Networks XV網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Complex Networks XV被引頻次




書(shū)目名稱(chēng)Complex Networks XV被引頻次學(xué)科排名




書(shū)目名稱(chēng)Complex Networks XV年度引用




書(shū)目名稱(chēng)Complex Networks XV年度引用學(xué)科排名




書(shū)目名稱(chēng)Complex Networks XV讀者反饋




書(shū)目名稱(chēng)Complex Networks XV讀者反饋學(xué)科排名

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Inhomogenous Marketing Mix Diffusion,ous) MMD model is an innovation diffusion model, similar to the Bass model, which includes four decision variables (the 4Ps of Marketing: Product, Price, Place, Promotion). We introduce the Inhomogenous MMD (IMMD) model and we conduct two separate experiments: one based on simulation and another one

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,Computing Motifs in?Hypergraphs,nal units. Recently, its extraction has been performed on higher-order networks, but due to the complexity arising from polyadic interactions, and the similarity with known computationally hard problems, its practical application is limited. Our main contribution is a novel approach for hyper-subgra

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,Expressivity of Geometric Inhomogeneous Random Graphs—Metric and Non-metric,amework for systematic evaluation of the expressivity of random graph models. We extend this framework to Geometric Inhomogeneous Random Graphs (GIRGs). This includes a family of graphs induced by non-metric distance functions which allow capturing more complex models of partial similarity between n

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Social Interactions Matter: Is Grey Wolf Optimizer a Particle Swarm Optimization Variation?,ame time, some can resemble similar computational performances regardless of their inspirations. To understand the mechanisms of such similarities, recent works have analyzed and compared swarm-based algorithms via a network based on the information flow shared collectively. Here, we modeled network

GILD

,Exploring Ingredient Variability in?Classic Russian Cuisine Dishes Through Complex Network Analysislizing a network analysis approach, we scrutinized 460 Olivier salad ingredient lists, alongside 97 vinegret and 127 okroshka ingredient lists, collected through online surveys. The findings highlight the vast diversity and regional variations in ingredient selection, emphasizing the adaptability of

maroon

,Unraveling the?Structure of?Knowledge: Consistency in?Everyday Networks, Diversity in?Scientific,in the realm of knowledge evolution and organisation. To that end, we look at using concept networks to capture the associations between these concepts as a domain grows. We compare concept networks as they grow for scientific domains, sci-fi literature, common news topics and science news, using Qu