Characteristics Of Complex Networks Research Articles

Study on the Monitoring and Guidance of Public Opinion of Micro-Blog Based on Complex Network in Colleges and Universities

With the advent of the information age, the Internet has become the most important platform for people to express their opinions and attitudes toward public affairs or hot emergencies. Micro-blog public opinion has become the most influential network of public opinion of the means of transmission, with a short time, a wide range of communication features. Based on the analysis of complex network characteristics and network public opinion, this paper analyzes the complex network topological properties of complex network using the statistical properties of complex networks, the number of nodes, the point intensity and the average weighted clustering coefficient. Choosing heat and situation And other indicators and node number, degree distribution and average weighted clustering coefficient of these three complex network parameters to achieve one-to-one mapping, and network life cycle of public opinion four-stage characteristics of building an emergency network public opinion monitoring and guidance model, public opinion real-time Monitoring, to determine the appropriate public opinion to guide the strategy. The actual public opinion monitoring and guidance through actual cases and real data verify the feasibility of the network public opinion monitoring and guidance model.

Autoscoring Essays Based on Complex Networks

This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale‐free networks (e.g., a word adjacency model) from which typical network features, such as the in‐/out‐degrees, clustering coefficient (CC), and dynamic networks, were obtained. The CDES integrates the classical concepts of network feature representation and essay score series variation. Several experiments indicated that the network measures different essay qualities and can be clearly demonstrated to develop complex networks for autoscoring tasks. The average agreement of the CDES and human rater scores was 86.5%, and the average Pearson correlation was .77. The results indicate that the CDES produced functional complex systems and autoscored Chinese essays in a method consistent with human raters. Our research suggests potential applications in other areas of educational assessment.

Model-guided combinatorial optimization of complex synthetic gene networks.

Constructing gene circuits that satisfy quantitative performance criteria has been a long‐standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three‐gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high‐throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model‐guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.

Trade-offs between robustness and small-world effect in complex networks.

Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. However, little is known about the relation between them. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We suggest that the robustness-oriented optimization will weaken the small-world effect and vice versa. Then, we propose a multi-objective trade-off optimization model and develop a heuristic algorithm to obtain the optimal trade-off topology for robustness and small-world effect. We show that the optimal network topology exhibits a pronounced core-periphery structure and investigate the structural properties of the optimized networks in detail.

Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability.

We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.

A multi-similarity spectral clustering method for community detection in dynamic networks.

Community structure is one of the fundamental characteristics of complex networks. Many methods have been proposed for community detection. However, most of these methods are designed for static networks and are not suitable for dynamic networks that evolve over time. Recently, the evolutionary clustering framework was proposed for clustering dynamic data, and it can also be used for community detection in dynamic networks. In this paper, a multi-similarity spectral (MSSC) method is proposed as an improvement to the former evolutionary clustering method. To detect the community structure in dynamic networks, our method considers the different similarity metrics of networks. First, multiple similarity matrices are constructed for each snapshot of dynamic networks. Then, a dynamic co-training algorithm is proposed by bootstrapping the clustering of different similarity measures. Compared with a number of baseline models, the experimental results show that the proposed MSSC method has better performance on some widely used synthetic and real-world datasets with ground-truth community structure that change over time.

Connectome analysis for pre-operative brain mapping in neurosurgery

Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations.Methods: Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal–parietal–occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series.Results: Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location.Conclusions: Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits.

Hierarchical Community Detection Algorithm Based on Node Similarity

Although community, one of the general characteristics of complex networks, has obvious hierarchical structure, in-depth research on its application in the current community detection algorithms is limited. In this paper we present a novel hierarchical community detection algorithm which starts from the node similarity calculation based on local adjacency in networks. Then we find the initial communities and joint them one by one as starting point from the bottom all the way to the top until all nodes fall within the same community. The concept of community similarity is defined for better community incorporation and each time, initial communities with the most similarity are integrated. Taking the advantage of time efficiency in local community mining, this kind of algorithm recognizes the community structure accurately when revealing the hierarchical structure of community, as the experiment shows.

A Michigan memetic algorithm for solving the community detection problem in complex network

Community structure is an important feature in complex networks which has great significant for organization of networks. The community detection is the process of partitioning the network into some communities in such a way that there exist many connections in the communities and few connections between them. In this paper a Michigan memetic algorithm; called MLAMA-Net; is proposed for solving the community detection problem. The proposed algorithm is an evolutionary algorithm in which each chromosome represents a part of the solution and the whole population represents the solution. In the proposed algorithm, the population of chromosomes is a network of chromosomes which is isomorphic to the input network. Each node has a chromosome and a learning automaton (LA). The chromosome represents the community of corresponding node and saves the histories of exploration. The learning automaton represents a meme and saves the histories of the exploitation. The proposed algorithm is a distributed algorithm in which each chromosome locally evolves by evolutionary operators and improves by a local search. By interacting with both the evolutionary operators and local search, our algorithm effectively detects the community structure in complex networks and solves the resolution limit problem of modularity optimization. To show the superiority of our proposed algorithm over the some well-known algorithms, several computer experiments have been conducted. The obtained results show MLAMA-Net is effective and efficient at detecting the community structure in complex networks.

Weighted-spectral clustering algorithm for detecting community structures in complex networks

The community structure is a common non-trivial topological feature of many complex real-world networks. Existing methods for identifying the community structure are generally based on statistical-type properties, such as the degree of centrality, the shortest path betweenness centrality, the modularity, and so forth. However, the form of the community structure may vary widely, even if the number of vertices and edges are fixed. Consequently, it is difficult to be certain of the exact number of clusters within the network. Clustering schemes which require the number of clusters to be specified in advance often misjudge the community structure and yield a poor clustering performance as a result. Accordingly, the present study proposes a clustering algorithm, designated as the Weighted-Spectral Clustering Algorithm, capable of detecting the community structure of a network with no prior knowledge of the cluster number. The proposed method is tested on both computer-generated networks and several real-world networks for which the community structures are already known. The results confirm the ability of the proposed algorithm to partition the network into an appropriate number of clusters in every case.

Detecting Community Structure by Using a Constrained Label Propagation Algorithm.

Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks gained immense interest over the last decade. A lot of community detection methods were proposed, and one of them is the label propagation algorithm (LPA). The simplicity and time efficiency of the LPA make it a popular community detection method. However, the LPA suffers from instability detection due to randomness that is induced in the algorithm. The focus of this paper is to improve the stability and accuracy of the LPA, while retaining its simplicity. Our proposed algorithm will first detect the main communities in a network by using the number of mutual neighbouring nodes. Subsequently, nodes are added into communities by using a constrained LPA. Those constraints are then gradually relaxed until all nodes are assigned into groups. In order to refine the quality of the detected communities, nodes in communities can be switched to another community or removed from their current communities at various stages of the algorithm. We evaluated our algorithm on three types of benchmark networks, namely the Lancichinetti-Fortunato-Radicchi (LFR), Relaxed Caveman (RC) and Girvan-Newman (GN) benchmarks. We also apply the present algorithm to some real-world networks of various sizes. The current results show some promising potential, of the proposed algorithm, in terms of detecting communities accurately. Furthermore, our constrained LPA has a robustness and stability that are significantly better than the simple LPA as it is able to yield deterministic results.

Finding overlapping communities based on Markov chain and link clustering

Since community structure is an important feature of complex network, the study of community detection has attracted more and more attention in recent years. Despite most researchers focus on identifying disjoint communities, communities in many real networks often overlap. In this paper, we proposed a novel MCLC algorithm to discover overlapping communities, which using random walk on the line graph and attraction intensity. Unlike traditional random walk starting from a node, our random walk starts from a link. First we transform an undirected network graph to a weighted line graph, and then random walks on this line graph can be associated with a Markov chain. By calculating the transition probability of the Markov chain, we obtain the similarity between link pairs. Next the links can be clustered into “link communities” by a linkage method, and these nodes between link communities can be overlapping nodes. When converting the “link communities” into the “node communities”, we make a definition of attraction intensity to control the overlapping size. Finally the detected communities are permitted overlapped. Experiments on synthetic networks and some real world networks validate the effectiveness and efficiency of the proposed algorithm. Comparing overlapping modularity Q o v with other related algorithms, the results of this algorithm are satisfactory.

Dynamical detection of network communities.

A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.

Synergetic Innovation in Social Governance in a Complex Network Structural Paradigm

Society is in essence a complex network system that is open, evolving and adaptable and possesses a coupling function. Social governance is a large-scale and complex systems engineering project. Due to the complexity and uncertainty of the environment and social affairs, the traditional government-led linear management model cannot provide effective interpretations and responses to complex social issues, and it is thus necessary to introduce the complexity scientific paradigm. Complexity science, especially complex system theory, has an intrinsic correspondence with social governance, and can reveal the inherent occurrence mechanism and rules brought about by the complexity of social governance. It indicates that the more complex a system is, the greater will be its requirements for systemic coordination and the more apparent its synergetic effects. A complex social network system is small-world and scale-free, with a community structure, preferential attachment and a topological structure characterized by the duality of virtuality and actuality. All of these have a direct and profound effect on social governance. To strengthen and innovate social governance, it is necessary to analyze the social system’s complex network structure and characteristics, establish synergetic and innovative mechanisms and institutional arrangements for social governance, and carry out synergetic social governance.

Combined neighborhood tabu search for community detection in complex networks

Community is one prominent feature of complex networks. Community detection is one important research topic in the area of complex networks analysis. In this paper, we introduce a new heuristic algorithm for community detection using the popular modularity measure. The proposed algorithm, called CNTS for combined neighborhood tabu search (CNTS), relies on a joint use of vertex move and merge operators to improve the quality of visited solutions. A dedicated tabu mechanism provides the algorithm with additional capacities to effectively explore the search space. Experiments using a collection of 21 well-known benchmark instances show that the proposed algorithm competes favorably with state-of-the-art algorithms.

Characterizing the topological and controllability features of U.S. power transmission networks

Understanding the controllability of complex networks continues to gain traction across disciplinary fields, including the exploration of infrastructure systems such as power grids. Through topological principles, this paper investigates the controllability features of an ensemble of 58 U.S. city-level power transmission networks in seven U.S. states. To perform structural controllability analyses, the topological characteristics of the ensemble of networks are first quantified, including degree, shortest path length, clustering coefficient, meshedness and betweenness centrality, as well as the uncertainty associated with these and related properties. Then, the paper focuses on the controllability features of complex networks so as to detect the minimal sets of driver nodes to possibly control the networks given system linearity assumptions. Accordingly, a node is critical, intermittent or redundant if it acts as a driver node in all, some, or none of the potentially controllable system configurations. Moreover, this paper constructs a new methodology to quantify the probability of being a driver node among the intermittent nodes, and reveals the controllability importance of system components. Results show that a small proportion of driver nodes can provide the conditions for controlling the slow dynamics of entire power transmission networks from a topological perspective, despite variations in network sizes and configurations. This paper also reveals that the driver nodes tend to avoid high degree nodes and high triangulation sub-graph nodes as well as high betweenness centrality nodes. The identification of topological differences for different categories of nodes (critical, intermittent or redundant) could help researchers and utilities understand the conditions for future functional controllability of power networks while improving their reliability and resilience as well as facilitating their transition into smart grid systems.

Topological characteristics of simple and complex normal fault networks

2-D, map-view topological analysis of ten natural and two analogue fault networks was undertaken. The fault arrays range from simple, low-displacement systems, to complex systems arising from multiple stages of deformation, or exhibiting complex local rotation of stresses. Classification of fault arrays was based on fault terminations (I-nodes), splaying and abutting geometries (Y-nodes) and cross-cutting relationships (X-nodes), which permit relatively quick and simple ways of analysing fault terminations and connectivity. Many of the fault networks are predominantly composed of I- and Y-nodes with at most only a minor X-node population, hence discrimination of significant differences between fault networks using just this type of analysis is limited. Subdividing Y-nodes into splaying (Ys), abutting (Ya) and cross-cutting (Yc) types, displaying the data on Ys-Ya-Yc node triangles, as well as generating equivalent networks defined by vertices and edges provides additional information for defining fault networks. Comparison of the Ys-Ya-Yc node triangle and the excess kurtosis of vertice degree distribution identifies seven distinct types of network that show meaningful differences. Such quantitative descriptions are useful for comparing the results of analogue and numerical models with natural examples as well as assessing fault network connectivity, which has implications for the structural interpretation of reservoirs and aquifers. A wide variety of factors contribute to variations in fault networks such as variations in strain, stress rotation with time, fabric inheritance, and stress deflection. While topology cannot be used to identify specific mechanisms, some topological characteristics can help narrow the likely mechanism particularly when used in conjunction with more traditional techniques and observations.

Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy

Epilepsy detection from electrical characteristics of EEG signals obtained from the brain of undergone subject is a challenge task for both research and neurologist due to the non-stationary and chaotic nature of EEG signals. As epileptic EEG signals contain huge fluctuating information about the functional behavior of the brain, it is hard to distinguish the fundamental dynamic, complex network of EEG signals without considering the strength among the nodes as they are connected with each other on the basis of these strengths. The prior research on natural visibility graph did not consider this issue in epileptic seizure, although it is a very important key point to have representative information from the signals. Hence, this paper aims to introduce a new idea for epilepsy detection using complex network statistical properties by measuring different strengths of the edges in natural visibility graph theory, which is considered as weight. Thus, the proposed method is named “weighted visibility graph”. In this proposed method, first, the epileptic EEG signals are transformed into complex network and then two important statistical properties of a network such as modularity and average weighted degree used for extracting the imperative characteristics from a network of EEG signals. After that, the extracted features are evaluated by two modern machine-learning classifiers such as, support vector machine with a different kernel function and k-nearest neighbor. The experimental results demonstrate that the combined effect of both features is valuable for network metrics to characterize the EEG time series signals in case of weighted complex network generating up to 100% classification accuracy.

Decreased Functional Connectivity in Schizophrenia: The Relationship Between Social Functioning and Graph Theoretical Network Measures

Event Abstract Back to Event Decreased Functional Connectivity in Schizophrenia: The Relationship Between Social Functioning and Graph Theoretical Network Measures Burak Erdeniz1*, Emin Serin1, Yelda İbadi2 and Cumhur Tas2 1 İzmir University of Economics, Psychology, Türkiye 2 Üsküdar University, Türkiye Background: Schizophrenia is a complex disorder where abnormalities in brain connectivity and social functioning play a central role. Currently, brain structures and mechanisms related to social functioning in schizophrenia are being intensively studied. However, the relationship between social functioning, and topological properties of brain functional networks remain still not well understood. The aim of this study is to explore the relationship between complex network characteristics such as small-world network properties and social functioning in schizophrenia. Methods: We tested this hypothesis by comparing differences in functional network topology and its correlation to clinical and behavioral measures in 23 patients diagnosed with Schizophrenia and 23 healthy controls during resting state. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia (14 male, 9 female) and 23 healthy volunteers (14 male, 9 female). Results: The results revealed that patients with schizophrenia show significantly decreased connectivity in a diversity of brain regions that include mainly the right orbito-frontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia characterized by reduced average path length compared to Healthy controls but no significant difference was found for clustering coefficient, local efficiency and global efficiency. However, when we analyzed nodal efficiency and nodal degree, we found significantly lower nodal efficiency and nodal –degree particularly in bilateral putamen and left amygdala in patients with Schizophrenia. Conclusion: These findings are consistent with previous studies that support disconnectivity hypothesis. The current results will help us to improve our understanding about the missing link between social cognition deficits and topological brain measures in Schizophrenia. Keywords: default-mode, graph theory, fMRI, small-world, Schizophrenia Keywords: Default-mode, graph theory, fMRI, Small-world, Schizophrenia Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016. Presentation Type: Oral Presentation in SAN 2016 Conference Topic: Oral Presentations Citation: Erdeniz B, Serin E, İbadi Y and Tas C (2016). Decreased Functional Connectivity in Schizophrenia: The Relationship Between Social Functioning and Graph Theoretical Network Measures. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00074 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 01 Aug 2016; Published Online: 01 Aug 2016. * Correspondence: Dr. Burak Erdeniz, İzmir University of Economics, Psychology, İzmir, Türkiye, burak.erdeniz@ieu.edu.tr Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas Google Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas Google Scholar Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas PubMed Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Improved community mining method based on LFM and EAGLE

Community structures are crucial topological characteristic of complex networks. Consequently, network community structure mining has great significance to the real world. Complex networks have both hierarchy and overlaps, but it is still a problem to excavate the hierarchy and overlaps of networks efficiently and accurately at the same time by algorithm. This paper proposes an efficient and functional complex network community partition algorithm by combining fitness function optimization and community similarity, which can uncover both overlapping and hierarchical community structure of complex networks. Its basic idea is to use fitness function optimization at the bottom of hierarchy division to identify efficiently and accurately the underlying community structure which is with overlaps. Hierarchical structure is based on the community similarity to merge the underlying sub-communities with the principle of maximum similarity circulation. The experimental results utilizing Karate Club Network and US college football network show that the proposed algorithm is a manageable and accurate method for not only discovering the gradation community structure, but also overlap between excavated club.