Synthetic Networks Research Articles

The disruption index is biased by citation inflation

Abstract A recent analysis of scientific publication and patent citation networks by Park et al. (Nature, 2023) suggests that publications and patents are becoming less disruptive over time. Here we show that the reported decrease in disruptiveness is an artifact of systematic shifts in the structure of citation networks unrelated to innovation system capacity. Instead, the decline is attributable to ‘citation inflation’, an unavoidable characteristic of real citation networks that manifests as a systematic time-dependent bias and renders cross-temporal analysis challenging. One driver of citation inflation is the ever-increasing lengths of reference lists over time, which in turn increases the density of links in citation networks, and causes the disruption index to converge to 0. The impact of this systematic bias further stymies efforts to correlate disruption to other measures that are also time-dependent, such as team size and citation counts. In order to demonstrate this fundamental measurement problem, we present three complementary lines of critique (deductive, empirical and computational modeling), and also make available an ensemble of synthetic citation networks that can be used to test alternative citation-based indices for systematic bias. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00333

Developmental assembly of multi-component polymer systems through interconnected synthetic gene networks in vitro

Living cells regulate the dynamics of developmental events through interconnected signaling systems that activate and deactivate inert precursors. This suggests that similarly, synthetic biomaterials could be designed to develop over time by using chemical reaction networks to regulate the availability of assembling components. Here we demonstrate how the sequential activation or deactivation of distinct DNA building blocks can be modularly coordinated to form distinct populations of self-assembling polymers using a transcriptional signaling cascade of synthetic genes. Our building blocks are DNA tiles that polymerize into nanotubes, and whose assembly can be controlled by RNA molecules produced by synthetic genes that target the tile interaction domains. To achieve different RNA production rates, we use a strategy based on promoter “nicking” and strand displacement. By changing the way the genes are cascaded and the RNA levels, we demonstrate that we can obtain spatially and temporally different outcomes in nanotube assembly, including random DNA polymers, block polymers, and as well as distinct autonomous formation and dissolution of distinct polymer populations. Our work demonstrates a way to construct autonomous supramolecular materials whose properties depend on the timing of molecular instructions for self-assembly, and can be immediately extended to a variety of other nucleic acid circuits and assemblies.

Shortest path counting in complex networks based on powers of the adjacency matrix.

Complex networks describe a broad range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a crucial role in network science, where the computation of shortest path lengths and numbers has garnered substantial focus. It is well known that powers of the adjacency matrix can calculate the number of walks, specifying their corresponding lengths. However, developing methodologies to quantify both the number and length of shortest paths through the adjacency matrix remains a challenge. Here, we extend powers of the adjacency matrix from walks to shortest paths. We address the all-pairs shortest path count problem and propose a fast algorithm based on powers of the adjacency matrix that counts both the number and the length of all shortest paths. Numerous experiments on synthetic and real-world networks demonstrate that our algorithm is significantly faster than the classical algorithms across various network types and sizes. Moreover, we verified that the time complexity of our proposed algorithm significantly surpasses that of the current state-of-the-art algorithms. The superior property of the algorithm allows for rapid calculation of all shortest paths within large-scale networks, offering significant potential applications in traffic flow optimization and social network analysis.

A nonlinear spectral core–periphery detection method for multiplex networks

Core–periphery detection aims to separate the nodes of a complex network into two subsets: a core that is densely connected to the entire network and a periphery that is densely connected to the core but sparsely connected internally. The definition of core–periphery structure in multiplex networks that record different types of interactions between the same set of nodes on different layers is non-trivial since a node may belong to the core in some layers and to the periphery in others. We propose a nonlinear spectral method for multiplex networks that simultaneously optimizes a node and a layer coreness vector by maximizing a suitable non-convex homogeneous objective function by a provably convergent alternating fixed-point iteration. We derive a quantitative measure for the quality of a given multiplex core–periphery structure that allows the determination of the optimal core size. Numerical experiments on synthetic and real-world networks illustrate that our approach is robust against noisy layers and significantly outperforms baseline methods while improving the latter with our novel optimized layer coreness weights. As the runtime of our method depends linearly on the number of edges of the network, it is scalable to large-scale multiplex networks.

Structural characterisation for the synthesis of large‐scale combined electric–gas networks

AbstractIn this paper, the authors present a methodology for building realistic synthetic test cases to model combined electric and natural gas infrastructure networks. Because these networks are synthetic, that is, fictitious, they are able to be freely shared; hence, they serve to support research and development in the area of coupled‐infrastructure analysis with large‐scale realistic test cases that do not contain non‐public information. We anchor our process for building these synthetic networks in a structural characterisation of actual electric and natural gas networks. Supply and demand nodes are geographically placed, based on a combination of publicly available information from several sources and a clustering‐based method to match the right fraction of each node type, taking into account the intersection points between the electric and gas grids. Then the networks are connected with pipelines and transmission lines using a systematic graph construction method, validated against network properties including degree distribution, clustering, graph diameter, and degree of planarity, along with operational validation to ensure the simulated solution is realistic. The methodology is demonstrated by creating and validating a test case with 6717 electric buses and 2451 gas nodes.

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

A control measure for epidemic spread based on the susceptible–infectious–susceptible (SIS) model

When an epidemic occurs in a network, finding the important links and cutting them off is an effective measure for preventing the spread of the epidemic. Traditional methods that remove important links easily lead to a disconnected network, inevitably incurring high costs arising from quarantining individuals or communities in a real-world network. In this study, we combine the clustering coefficient and the eigenvector to identify the important links using the susceptible–infectious–susceptible (SIS) model. The results show that our approach can improve the epidemic threshold while maintaining the connectivity of the network to control the spread of the epidemic. Experiments on multiple real-world and synthetic networks of varying sizes, demonstrate the effectiveness and scalability of our approach.

Improving community detection in blockmodel by distance-based observation selection

Community detection is an important research topic in complex systems and has plenty of applications in real-world networks. Probabilistic methods, such as the Expectation–Maximization (EM), are developed to classify nodes that have similar connection patterns in a network based on blockmodels. However, the detection procedures in these models are typically started from randomly generated initial community distributions without prior knowledge. In biological and social networks, there are practical measures to obtain prior knowledge for a subset of nodes, such as local observations. These facts lead us to question how we can select a subset of nodes with known community labels to enhance the accuracy of the EM method. The current selection methods lack the relationship between detection accuracy and structural characteristics and most approaches consider the nodes as the center of communities, which is not suitable for block models. In this paper, we first study the relationships between the structural distance and detection accuracy without prior knowledge. Then we propose a distance-based indicator to describe the performance of the observation node set in the EM method. Finally, we introduce a scoring method based on the indicator to select a partial observation set, improving the accuracy of community detection using the EM method. Empirical results from synthetic and real-world networks corroborate that the proposed indicator could contribute to a better performance in kinds of scenarios.

Approximate IsoRank for Scalable and Functionally Meaningful Cross-Species Alignments of Protein Interaction Networks.

The IsoRank algorithm of Singh, Xu, and Berger was a pioneering algorithmic advance that applied spectral methods to the problem of cross-species global alignment of biological networks. We develop a new IsoRank approximation that exploits the mathematical properties of IsoRank's linear system to solve the problem in quadratic time with respect to the maximum size of the two protein-protein interaction (PPI) networks. We further propose a refinement to this initial approximation so that the updated result is even closer to the original IsoRank formulation while remaining computationally inexpensive. In experiments on synthetic and real PPI networks with various proposed metrics to measure alignment quality, we find the results of our approximate IsoRank are nearly as accurate as the original IsoRank. In fact, for functional enrichment-based measures of global network alignment quality, our approximation performs better than the exact IsoRank, which is doubtless because it is more robust to the noise of missing or incorrect edges. It also performs competitively against two more recent global network alignment algorithms. We also present an analogous approximation to IsoRankN, which extends the network alignment to more than two species.

A two-step linear programming approach for repeater placement in large-scale quantum networks

Thanks to the applications such as Quantum Key Distribution and Distributed Quantum Computing, the deployment of quantum networks is gaining great momentum. A major component in quantum networks is repeaters, which are essential for reducing the error rate of qubit transmission for long-distance links. However, repeaters are expensive devices, so minimizing the number of repeaters placed in a quantum network while satisfying performance requirements becomes an important problem. Existing solutions typically solve this problem optimally by formulating an Integer Linear Program (ILP). However, the number of variables in their ILPs is O(n2), where n is the number of nodes in a network. This incurs infeasible running time when the network scale is large. To overcome this drawback, this paper proposes to solve the repeater placement problem by two steps, with each step using a linear program of a much smaller scale with O(n) variables. Although this solution is not optimal, it dramatically reduces the time complexity, making it practical for large-scale networks. Moreover, it constructs networks that have higher node connectivity than those by existing solutions, since it deploys slightly more number of repeaters into networks. Our extensive experiments on both synthetic and real-world network topologies verified our claims.

Exploring the Field of Virtual Avatar: A Bibliometric and Content Analysis

The virtual avatar (VA) concept has gained significant attention since the introduction of virtual products. This study aims to investigate the present level of advancement and forthcoming research directions in the domain of VA while also constructing a conceptual framework for VA. A thorough examination of the VA field was carried out by integrating bibliometric and content analytic techniques. Eight hundred ninety-nine journal articles underwent descriptive bibliometric analysis using CiteSpace and VOSviewer software to construct and visualize synthetic networks. Two hundred forty-eight documents were selected for content analysis. VA is mostly focused on a few specific disciplines and journals prioritizing innovation. Conducting empirical investigations in many settings can enhance the research areas of Virtual Reality (VR), Augmented Reality (AR), and Extended Reality (XR), including improving algorithms, exploring accessibility usage, and examining public service applications. Alternative theoretical frameworks can provide novel insights and additional directions for the development of the VA field.

Hermitian random walk graph Fourier transform for directed graphs and its applications

Signal processing on directed graphs present additional challenges since a complete set of eigenvectors is unavailable generally. To solve this problem, in this paper, a novel graph Fourier transform is constructed for representing and processing signals on directed graphs. Firstly, we introduce a Hermitian random walk Laplacian operator and derive that it is Hermitian positive semi-definite. Hence, the obtained Laplacian operator is diagonalizable and yields orthogonal eigenvectors as graph Fourier basis. Secondly, we propose the Hermitian random walk graph Fourier transform (HRWGFT) with good properties including unitary and preserving inner products. Furthermore, HRWGFT records the directionality of edges without sacrificing the information about the graph signal. Then, using these favorable properties, we derive spectral convolution to define the graph filter which is the core tool for processing graph signals. Finally, based on the proposed Laplacian matrix and HRWGFT, we present several applications on synthetic and real-world networks, including signal denoising, data classification. The rationality and validity of our work are verified by simulations.

In-depth Investigation into Supervised and Unsupervised Models within Artificial Neural Networks

An Artificial Neural Network (ANN) is an information-handling worldview that is jazzed up by exactly how natural nerves, like the mind, procedure details. The essential element of this particular worldview is the initial Building of the information dealing with the unit. A neural network is a difficult development that comprises a gathering of interconnected nerve cells, which offers a remarkably electrifying possibility unlike complicated essential reasoning and also various other treatments which may think a notable component in today's software engineering industry, thus professionals from different fields are organizing the synthetic neural networks to tackle the concerns of trend awareness, prophecy, optimization, associative moment and command. It is constructed of an enormous lot of specially connected taking care of elements (neurons) functioning as one to address explicit issues. ANNs, identical to individuals, innovation as an aesthetic presentation. An ANN is developed for a specific app, for example, pattern recognition or records classification, using developing knowledge. Discovering in biological units features modifications following the synaptic associations between neurons.

A Dominance Approach for Influence Maximization with Incomplete Information in Social Network

Influence maximization plays an important role in social network analysis and has been extensively explored due to its emergence in a great deal of applications. However, a vast majority of researchers have only considered the influence of one factor on the spread of information among individuals. In addition, this factor which is mainly the influence probability, has been considered to be fixed which is estimated based on the number of neighbors of each individual. While, in reality, we do not have complete information about this probability and it is not a fixed value. Moreover, in the spread of information, other factors including trust, are also involved, which cannot be easily combined due to their different nature. In this paper, a dominance approach to influence maximization is studied under the condition, in which, complete information about influence of users on each other is not available and users infect each other based on an unknown influence probability. Furthermore, in order to make the proposed model closer to reality, trust factor is also considered as an independent factor. The proposed method is compared to several state-of-the-art influence maximization algorithms and attained results on several synthetic and real social network datasets verify effectiveness of the proposed method compared to previous methods in the context of influence maximization.

ELSNC: A semi-supervised community detection method with integration of embedding-enhanced links and node content in attributed networks

In complex network analysis, detecting communities is becoming increasingly important. However, it is difficult to fuse multiple types of information to enhance the community-detection performance in real-world applications. Besides the nodes and the edges, a network also contains the structure of communities, its networking topological structure, and the network embeddings. Note that existing works on community detection have limited usage of all these information types in combination. In this work, we designed a novel unified model called embedding-enhanced link-based semi-supervised community detection with node content (ELSNC). ELSNC integrates the structure of the topology, the priori information, the network embeddings, and the node content. First, we employ two non-negative matrix factorization (NMF)–based stochastic models to characterize the node-community membership and the content-community membership (by performing similarity detection between a topic model and the NMF). Second, we introduce the nodes’ and networking embeddings’ topological similarity into the model as topological information. To model the topological similarity, we introduce a strong constraint (i.e., the priori information) and apply matrix completion to identify the community membership with the network embeddings’ representation ability. Finally, we present a semi-supervised community-detection method based on NMF that combines the network topology, content information, and the network embeddings. Our work’s innovation can be captured in two points: 1) As a type of semi-supervised community detection method, we extend the theory of semi-supervised methods on attributed networks and propose a unified model that integrates multiple information types. 2) The community membership obtained by the unified model simultaneously contains different information, including the topological, content, priori, and embedding information, which can more robustly be explored in the community structure in real-world scenarios. Furthermore, we performed a comprehensive evaluation of our proposed approach compared with state-of-the-art methods on both synthetic and real-world networks. The results show that our proposed method significantly outperformed the baseline methods.

Broken detailed balance and entropy production in directed networks.

The structure of a complex network plays a crucial role in determining its dynamical properties. In this paper, we show that the the degree to which a network is directed and hierarchically organized is closely associated with the degree to which its dynamics break detailed balance and produce entropy. We consider a range of dynamical processes and show how different directed network features affect their entropy production rate. We begin with an analytical treatment of a two-node network followed by numerical simulations of synthetic networks using the preferential attachment and Erdös-Renyi algorithms. Next, we analyze a collection of 97 empirical networks to determine the effect of complex real-world topologies. Finally, we present a simple method for inferring broken detailed balance and directed network structure from multivariate timeseries and apply our method to identify non-equilibrium dynamics and hierarchical organisation in both human neuroimaging and financial timeseries. Overall, our results shed light on the consequences of directed network structure on non-equilibrium dynamics and highlight the importance and ubiquity of hierarchical organisation and non-equilibrium dynamics in real-world systems.

Experimental Evaluation of the Effect of Community Structures on Link Prediction

Link prediction involves assessing the likelihood of connections between node pairs based on various structural properties. The effectiveness of link predictors can be influenced by complex structures such as communities. Since the community structure itself has different properties that describes its characteristics, measuring the impact of these properties on the performance of link predictors presents a challenge. In this work, we aim to uncover the role of community properties and the identification of community structures on the performance of link predictors. We propose a comprehensive experimental setup to evaluate the performance of twenty-nine link predictors on real-world networks with diverse topological features, as well as on synthetic networks where we control community-dependent properties such as cohesiveness and size. We assess the performance differences between network-wide and per-community link prediction to determine whether identifying communities aids in link prediction. The results indicate that link prediction is more accurate in networks with well-defined, disjoint communities, even when these communities are not explicitly identified. Additionally, the size of the communities can influence link prediction performance if the communities are identified.

Spatial network disintegration with heterogeneous cost

The problem of spatial network disintegration, such as the suppression of epidemic spread and the destabilization of terrorist networks, has garnered increasing interest. However, current methodologies often assume uniformity in disintegration costs across diverse areas, thereby simplifying the complexities of real-world scenarios. In reality, the costs of removing areas vary significantly due to geographical, economic, and structural differences. Here we draw attention to the spatial network disintegration with heterogeneous cost, where the disintegration cost to remove each area might be non-identical. We first develop a cost-constrained model based on the geospatial characteristics and introduce four typical strategies to identify crucial areas that maximize the effectiveness of the disintegration process. Experimental results on both synthetic and real-world networks indicate that while the effectiveness of the hub strategy can deteriorate under certain conditions, the average degree and leaf strategies may exhibit enhanced disintegration effects under specific parameters. This phenomenon fundamentally alters the identification of critical areas as disintegration costs transition from homogeneity to heterogeneity. These findings substantially enhance our understanding of spatial network robustness and provide a novel viewpoint for the protection of spatial networks.

Efficient estimation of the modified Gromov–Hausdorff distance between unweighted graphs

Gromov–Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g. point clouds, manifolds, or graphs. Computing any Gromov–Hausdorff distance is equivalent to solving an NP-hard optimization problem, deeming the notion impractical for applications. In this paper we propose a polynomial algorithm for estimating the so-called modified Gromov–Hausdorff (mGH) distance, a relaxation of the standard Gromov–Hausdorff (GH) distance with similar topological properties. We implement the algorithm for the case of compact metric spaces induced by unweighted graphs as part of Python library scikit-tda, and demonstrate its performance on real-world and synthetic networks. The algorithm finds the mGH distances exactly on most graphs with the scale-free property. We use the computed mGH distances to successfully detect outliers in real-world social and computer networks.

3D bioprinting of colon organoids in ultrashort self-assembling and decorated peptide matrices

Research into bioinks for organoid culture has the potential to revolutionize our understanding of organ development, function, and disease. Organoids are three-dimensional (3D) cultures of source tissue grown in a support matrix and specialized media. However, the use of animal-derived matrices has limited the potential of organoids in research and therapy. To overcome this limitation, researchers have turned to biofunctional synthetic hydrogel networks to reproduce parameters that govern organoid formation. This study aims to investigate RGD- and YIGSR-decorated ultrashort self-assembling peptides as a modular synthetic hydrogel for organoid culture and 3D bioprinting. Using these motifs, we derived fibronectin (FIB)- and laminin (LAM)-decorated peptides, which self-assemble into nanofibrous hydrogels. We assessed the physicochemical properties of various peptide mixtures. Our findings confirmed the biocompatibility of these formulations and their organoid-forming potential. Subsequently, we identified the most effective scaffolds for organoid formation. We assessed the polarity, differentiation, and functionality of organoids cultured within these scaffolds. We also characterized the properties of a bioprinted construct. This study identifies two formulations, FIB (low) and LAM (high), that favor cell polarization within the cultured organoids as early as day 4. Moreover, these scaffolds were able to induce a gene expression profile resembling the organoids cultured in Matrigel. These peptides were also demonstrated to be suitable for bioprinting at various concentrations without compromising cell viability. Overall, this study demonstrates the promise of modular RGD- and YIGSR-decorated ultrashort self-assembling peptides as effective synthetic hydrogels for organoid culture and 3D bioprinting. These biofunctional peptides provide scaffold effectiveness for advanced organoid manipulation.