Higher-Order Network Structure Embedding in Supply Chain Partner Link Prediction

2019 ◽  
pp. 3-17
Author(s):  
Miao Xie ◽  
Tengjiang Wang ◽  
Qianyu Jiang ◽  
Li Pan ◽  
Shijun Liu
Keyword(s):  
Supply Chain ◽  
Network Structure ◽  
Link Prediction ◽  
Higher Order

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

Complex Autocatalysis in Simple Chemistries

2016 ◽  
Vol 22 (2) ◽  
pp. 138-152 ◽  
Author(s):  
Nathaniel Virgo ◽  
Takashi Ikegami ◽  
Simon McGregor
Keyword(s):  
Network Structure ◽  
Exponential Growth ◽  
Origins Of Life ◽  
Higher Order ◽  
Nonlinear Phenomena ◽  
Direct Reaction ◽  
Form Complex ◽  
First Order ◽  
Life On Earth ◽  
Catalytic Effects

Life on Earth must originally have arisen from abiotic chemistry. Since the details of this chemistry are unknown, we wish to understand, in general, which types of chemistry can lead to complex, lifelike behavior. Here we show that even very simple chemistries in the thermodynamically reversible regime can self-organize to form complex autocatalytic cycles, with the catalytic effects emerging from the network structure. We demonstrate this with a very simple but thermodynamically reasonable artificial chemistry model. By suppressing the direct reaction from reactants to products, we obtain the simplest kind of autocatalytic cycle, resulting in exponential growth. When these simple first-order cycles are prevented from forming, the system achieves superexponential growth through more complex, higher-order autocatalytic cycles. This leads to nonlinear phenomena such as oscillations and bistability, the latter of which is of particular interest regarding the origins of life.

Let the buyer beware: how network structure can enable (and prevent) supply chain fraud

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Scott DuHadway ◽  
Carlos Mena ◽  
Lisa Marie Ellram
Keyword(s):  
Supply Chain ◽  
Network Structure ◽  
Structural Characteristics ◽  
Case Study Research ◽  
Structural Holes ◽  
Content Type ◽  
New Concepts ◽  
Bridge Collapse ◽  
Multiple Case

PurposeSupply chain fraud is a significant global concern for firms, consumers and governments. Evidence of major fraud events suggests the role of supply chain structures in enabling and facilitating fraud, as they often involve several parties in complicated networks designed to obfuscate the fraud. This paper identifies how the structural characteristics of supply chains can play an important role in enabling, facilitating and preventing fraud.Design/methodology/approachThe research follows a theory elaboration approach. The authors build on structural holes theory in conjunction with a multiple case study research design to identify new concepts and develop propositions regarding the role of network structure on supply chain fraud.FindingsThis research shows how structural holes in a supply chain can create advantages for unscrupulous firms, a role we call tertius fraudans, or the cheating third. This situation is exacerbated by structural ignorance, which refers to the lack of knowledge about structural connections in the network. Both structural holes and structural ignorance can create information gaps that facilitate fraud, and the authors propose solutions to detect and prevent this kind of fraud.Originality/valueThis paper extends structural holes theory into the domain of fraud. Novel concepts including tertius fraudans, structural ignorance and bridge collapse are offered, alongside a series of propositions that can help understand and manage structural supply chain fraud.

A transportation network evolution model based on link prediction

2021 ◽  
Author(s):  
Shuang Gu ◽  
Keping Li ◽  
Yan Liang ◽  
Dongyang Yan
Keyword(s):  
Network Structure ◽  
Link Prediction ◽  
Transportation Networks ◽  
Network Evolution ◽  
Evolution Model ◽  
Civil Aviation ◽  
Node Degree ◽  
Network Efficiency ◽  
Evolution Mechanism ◽  
Proposed Model

An effective and reliable evolution model can provide strong support for the planning and design of transportation networks. As a network evolution mechanism, link prediction plays an important role in the expansion of transportation networks. Most of the previous algorithms mainly took node degree or common neighbors into account in calculating link probability between two nodes, and the structure characteristics which can enhance global network efficiency are rarely considered. To address these issues, we propose a new evolution mechanism of transportation networks from the aspect of link prediction. Specifically, node degree, distance, path, expected network structure, relevance, population and GDP are comprehensively considered according to the characteristics and requirements of the transportation networks. Numerical experiments are done with China’s high-speed railway network, China’s highway network and China’s inland civil aviation network. We compare receiver operating characteristic curve and network efficiency in different models and explore the degree and hubs of networks generated by the proposed model. The results show that the proposed model has better prediction performance and can effectively optimize the network structure compared with other baseline link prediction methods.

scholarly journals Impact of higher order network structure on emergent cortical activity

2020 ◽  
Vol 4 (1) ◽  
pp. 292-314 ◽  
Author(s):  
Max Nolte ◽  
Eyal Gal ◽  
Henry Markram ◽  
Michael W. Reimann
Keyword(s):  
Network Structure ◽  
Morphological Diversity ◽  
Cortical Activity ◽  
Higher Order ◽  
Neuronal Firing ◽  
Network Activity ◽  
Synaptic Connectivity ◽  
Order Structure ◽  
First Order ◽  
Neuronal Types

Synaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher order network structure, and suggests that the higher order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.

When Ignorance Is Not Bliss: An Empirical Analysis of Subtier Supply Network Structure on Firm Risk

2020 ◽  
Author(s):  
Yixin (Iris) Wang ◽  
Jun Li ◽  
Di (Andrew) Wu ◽  
Ravi Anupindi
Keyword(s):  
Supply Chain ◽  
Network Structure ◽  
Operations Management ◽  
Financial Risk ◽  
Significant Proportion ◽  
Abnormal Returns ◽  
Supply Network ◽  
Tier 2 ◽  
Risk Levels ◽  
Common Structure

Using a multitier mapping of supply-chain relationships constructed from granular global, firm-to-firm supplier–customer linkages data, we quantify the degree of financial risk propagation from the supply network beyond firms’ direct supply-chain connections and isolate structural network properties serving as significant moderators of risk propagation. We first document a baseline fact: a significant proportion of tier-2 suppliers are shared by tier-1 suppliers. We then construct two simple metrics to capture the degree of tier-2 sharing and disentangle its effect from tier-2 suppliers’ own risks. We show that the focal firms’ risk levels are significantly related to the proportion of shared tier-2 suppliers in their supply network, and the effect becomes monotonically stronger as their tier-2 suppliers become more highly shared. Finally, we uncover causal relationships behind these associations using a new source of exogenous, idiosyncratic risk events in an event study setting. We show that, as tier-2 suppliers are impacted by these events, focal firms experience negative abnormal returns, the magnitude of which is significantly larger when the impacted tier-2 suppliers are more heavily shared. Overall, our study uncovers the subtier network structure as an important risk source for the focal firm, with the degree of tier-2 sharing as the main moderator. Our results also provide the microfoundation for a common structure in idiosyncratic risks and suggest the importance of incorporating the effect of subtier supply network structure in the portfolio-optimization process. This paper was accepted by Vishal Gaur, operations management.

scholarly journals GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i464-i473
Author(s):  
Kapil Devkota ◽  
James M Murphy ◽  
Lenore J Cowen
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
Global Network ◽  
Local Network ◽  
Supplementary Information ◽  
Network Data ◽  
Ppi Network ◽  
Diffusion State

Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online.

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