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Abstract Networks to distribute goods, from raw materials to food and medicines, are the backbone of a functioning economy. They are shaped by several supply relations connecting manufacturers, distributors, and final buyers worldwide. We present a network-based model to describe the mechanisms underlying the emergence and growth of distribution networks. In our model, firms consider two practices when establishing new supply relations: centralization, the tendency to choose highly connected partners, and multi-sourcing, the preference for multiple suppliers. Centralization enhances network efficiency by leveraging short distribution paths; multi-sourcing fosters resilience by providing multiple distribution paths connecting final buyers to the manufacturer. We validate the proposed model using data on drug shipments in the US. Drawing on these data, we reconstruct 22 nationwide pharmaceutical distribution networks. We demonstrate that the proposed model successfully replicates several structural features of the empirical networks, including their out-degree and path length distributions as well as their resilience and efficiency properties. These findings suggest that the proposed firm-level practices effectively capture the network growth process that leads to the observed structures.

Networks to distribute goods, from raw materials to food and medicines, are the backbone of a functioning economy. They are shaped by several supply relations connecting manufacturers, distributors, and final buyers worldwide. We present a network-based model to describe the mechanisms underlying the emergence and growth of distribution networks. In our model, firms consider two practices when establishing new supply relations: centralization, the tendency to choose highly connected partners, and multi-sourcing, the preference for multiple suppliers. Centralization enhances network efficiency by leveraging short distribution paths; multi-sourcing fosters resilience by providing multiple distribution paths connecting final buyers to the manufacturer. We validate the proposed model using data on drug shipments in the US. Drawing on these data, we reconstruct 22 nationwide pharmaceutical distribution networks. We demonstrate that the proposed model successfully replicates several structural features of the empirical networks, including their out-degree and path length distributions as well as their resilience and efficiency properties. These findings suggest that the proposed firm-level practices effectively capture the network growth process that leads to the observed structures. [ABSTRACT FROM AUTHOR]

The no restart random walk (NRRW) is a random network growth model driven by a random walk that builds the graph while moving on it, adding and connecting a new leaf node to the current position of the walker every s steps. We show a fundamental dichotomy in NRRW with respect to the parity of s : for ${s}=1$ we prove that the random walk is transient and non-leaf nodes have degrees bounded above by an exponential distribution; for s even we prove that the random walk is recurrent and non-leaf nodes have degrees bounded below by a power law distribution. These theoretical findings highlight and confirm the diverse and rich behaviour of NRRW observed empirically. [ABSTRACT FROM AUTHOR]

Duplication and divergence are two basic evolutionary mechanisms of bio-molecular networks. Real-world bio-molecular networks and their statistical characteristics can be well mimicked by artificial algorithms based on the two mechanisms. Bio-molecular networks consist of network motifs, which act as building blocks of large-scale networks. A fundamental question is how network motifs are evolved from long time evolution and natural selection. By considering the effect of various duplication and divergence strategies, we find that the underlying duplication scheme of the real-world undirected bio-molecular networks would rather follow the anti-preference strategy than the random one. The anti-preference duplication mechanism and the dimerization processes can lead to the formation of various motifs, and robustly conserve proper quantities of motifs in the artificial networks as that in the real-world ones. Furthermore, the anti-preference mechanism and edge deletion divergence can robustly preserve the sparsity of the networks. The investigations reveal the possible evolutionary mechanisms of network motifs in real-world bio-molecular networks, and have potential implications in the design, synthesis and reengineering of biological networks for biomedical purpose. [ABSTRACT FROM PUBLISHER]

Software engineering efforts can potentially benefit much from a good understanding of the structures of existing software systems and the processes governing their development. Towards that end, we study software systems by means of the complex network analysis framework. We model a software package as a network, with nodes representing the functions in the package and edges representing the dependencies among the functions. Our empirical analysis of five widely-adopted open-source software packages reveals a set of interesting features of such networks, which cannot be adequately reproduced by existing complex network models. We then set out to develop a new network growth model, explicitly imitating generally-advocated software development principals, such as divide-and-conquer, modularization, high intra-module cohesion, and low inter-module coupling. Results of our analytical derivations and numeric studies show that our model can more closely reproduce the particular features exhibited by real-world software packages, thus hopefully better explaining the phenomena of concern. [ABSTRACT FROM AUTHOR]

Aiming to construct a more precise probabilistic model for the short-term growth process of real-world growing networks such as the Web, we propose a new network growth model and its learning algorithm. Unlike the conventional network growth models, we incorporate directional attachment and community structure for this purpose. We first show that the proposed model also exhibits a degree distribution with a power-law tail. In experiments using real Web data, we demonstrate that prediction of the dynamics of a given growing network can be improved by incorporating directional attachment and community structure. In experiments using synthetic data, we also demonstrate that prediction performance can definitely be improved by incorporating community structure. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(8): 1–11, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10706 [ABSTRACT FROM AUTHOR]

Abstract Background The study of biological networks and how they have evolved is fundamental to our understanding of the cell. By investigating how proteins of different ages are connected in the protein interaction network, one can infer how that network has expanded in evolution, without the need for explicit reconstruction of ancestral networks. Studies that implement this approach show that proteins are often connected to proteins of a similar age, suggesting a simultaneous emergence of interacting proteins. There are several theories explaining this phenomenon, but despite the importance of gene duplication in genome evolution, none consider protein family dynamics as a contributing factor. Results In an S. cerevisiae protein interaction network we investigate to what extent edges that arise from duplication events contribute to the observed tendency to interact with proteins of a similar age. We find that part of this tendency is explained by interactions between paralogs. Age is usually defined on the level of protein families, rather than individual proteins, hence paralogs have the same age. The major contribution however, is from interaction partners that are shared between paralogs. These interactions have most likely been conserved after a duplication event. To investigate to what extent a nearly neutral process of network growth can explain these results, we adjust a well-studied network growth model to incorporate protein families. Our model shows that the number of edges between paralogs can be amplified by subsequent duplication events, thus explaining the overrepresentation of interparalog edges in the data. The fact that interaction partners shared by paralogs are often of the same age as the paralogs does not arise naturally from our model and needs further investigation. Conclusion We amend previous theories that explain why proteins of a similar age prefer to interact by demonstrating that this observation can be partially explained by gene duplication events. There is an ongoing debate on whether the protein interaction network is predominantly shaped by duplication and subfunctionalization or whether network rewiring is most important. Our analyses of S. cerevisiae protein interaction networks demonstrate that duplications have influenced at least one property of the protein interaction network: how proteins of different ages are connected.

Transport networks distribute resources and information in many human and biological systems. Their construction requires optimization and balance of conflicting criteria such as robustness against disruptions, transport efficiency and building cost. The colonies of the polydomous Australian meat ant Iridomyrmex purpureus are a striking example of such a decentralized network, consisting of trails that connect spatially separated nests. Here we study the rules that underlie network construction in these ants. We find that a simple model of network growth, which we call the minimum linking model (MLM), is sufficient to explain the growth of real ant colonies. For larger networks, the MLM shows a qualitative similarity with a Euclidean minimum spanning tree, prioritizing cost and efficiency over robustness. We introduce a variant of our model to show that a balance between cost, efficiency and robustness can be also reproduced at larger scales than ant colonies. Remarkably, such a balance is influenced by a parameter reflecting the specific features of the modelled transport system. The extended MLM could thus be a suitable source of inspiration for the construction of cheap and efficient transport networks with non-zero robustness, suggesting possible applications in the design of human-made networks., (© 2015 The Author(s).)