Narrow Results

Urban Park green space is an essential carrier and form of urban recreation space, playing an important role in improving the ecological environment and enhancing the image of the city. With the process of urbanization and the continuous improvement of residents’ living standards, urban park construction has become increasingly important to meet people’s growing needs for a better life. However, the evaluation indicators for urban park green space in China mostly consider macroscopic levels, such as the area, number and per capita green area of parks. This cannot fully reflect the level of park green space services. Accessibility can reflect the convenience of residents to reach the park, provide guidance for the reasonable layout of parks and have various evaluation methods relying on geographic information technology. Therefore, it is necessary to quantitatively study the layout and accessibility of park green space. Taking the central urban area of Nanjing as the research object, this paper summarizes existing research and focuses on evaluating the accessibility of park green space while analyzing the layout of parks from multiple perspectives, combined with the existing pressure appraisal of park green space services. Relevant data on Nanjing, including overall planning and statistical yearbooks, were collected and analyzed using geographic information system (GIS) tools, network analysis and Thiessen polygon theory to analyze the service pressure, demand and accessibility of park green space in the central urban area under different transportation methods. Based on the results, targeted optimization strategies are proposed.

In this paper, we investigate global connectedness and networks of agricultural production on continental and subregional levels. Using per capita agricultural production indices (API) from the United Nations Food and Agriculture Organization, we applied the spillover index method and network analysis. Continental-level analysis shows that global agricultural production is mainly connected to production in Europe and Asia. The subregional analysis also confirms that most subregions are connected to Europe and Asia regarding agricultural production. Agricultural production shocks occurring in Western Asia, Western Europe, Southern Europe, Southeast Asia and Eastern Asia regions have highly spread to other regions. This study demonstrates that worldwide agricultural production is highly interconnected and integrated. Based on these results, our study showed that global agricultural production has been converging. The findings of this study can be used by policymakers as well as national or international institutions shaping and regulating national and regional agricultural and economic policies.

Graph theory is employed in this study to examine the Paate clan of the Nyishi tribe’s unique naming system. The network represented by the Parental graph is a directed acyclic graph with 1388 nodes which represent individuals of the clan, and 1387 arcs indicate father–son relationships and the transfer of the naming system. Centrality measures show that individual Kadu is the most essential node for linking others, while individual Nina is capable of communicating more efficiently with other nodes in the network. No clusters or cliques were observed in the network. The network exhibits characteristics of a scale-free network. No relinking marriages were observed in the genealogical network.

Many centrality metrics have been proposed over the years to compute the centrality of nodes, which has been a key issue in complex network analysis. The most important node can be estimated through a variety of metrics, such as degree, closeness, eigenvector, betweenness, flow betweenness, cumulated nominations and subgraph. Simulated flow is a common method adopted by many centrality metrics, such as flow betweenness centrality, which assumes that the information spreads freely in the entire network. Generally speaking, the farther the information travels, the more times the information passes the geometric center. Thus, it is easy to determine which node is more likely to be the center of the geometry network. However, during information transmission, different nodes do not share the same vitality, and some nodes are more active than others. Therefore, the product of one node's degree and its clustering coefficient can be viewed as a good factor to show how active this node is. In this paper, a new centrality metric called vitality centrality is introduced, which is only based on this product and the simulated flow. Simulation experiments based on six test networks have been carried out to demonstrate the effectiveness of our new metric.

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these networks, drugs were ranked based on their centrality scores whereby 18 prominent drugs from the drug-side effect network and 15 prominent drugs from the drug-indication network were identified. Indications and side effects of prominent drugs were deduced from the profiles of their neighbors in the networks and compared to existing clinical studies while an optimum threshold of similarity among drugs was sought for. The threshold can then be utilized for predicting indications and side effects of all drugs. Similarities of drugs were measured by the extent to which they share phenotypic profiles and neighbors. To improve the likelihood of accurate predictions, only profiles such as side effects of common or very common frequencies were considered. In summary, our work is an attempt to offer an alternative approach to drug repositioning using centrality measures commonly used for analyzing social networks.

A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0MB, 180.0MB, and 360.0MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.

Acupuncture has been used as a therapeutic intervention for the treatment of numerous diseases and symptoms for thousands of years, and low back pain has been studied and treated the most in acupuncture clinics. Traditional theory strongly suggests that the selection of acupoints will influence their clinical effects and combinations (e.g., the clinical effects of a particular acupoint or combination on reducing pain), but this idea was not considered in earlier systematic reviews and meta-analyses. We performed a systematic review, meta-analysis, and network analysis to evaluate the magnitude of the effects of acupoints used to treat low back pain in randomized controlled clinical trials. We found that acupuncture significantly reduced pain in patients with low back pain compared with the control group. The most frequently prescribed acupoints were BL23, GV3, BL20, BL40, and BL25, whereas the acupoints with the highest average effect size scores were BL20, GV3, GB30, GB34, and BL25. Further, the combinations of BL23-BL40, BL23-B25, and BL23-BL60 were the most frequently prescribed, while BL23-GV3, BL40-GV4, and BL23-BL25 showed the largest average effect size. By calculating clinical outcomes based on average effect sizes, we found that the most popular acupoints might not always be associated with the best results. Although a more thorough investigation is necessary to determine the clinical effects of each acupoint and combination on patients, we suggest that our approach may offer a fresh perspective that will be useful for future research.

With the development of economy and society, network analysis is widely used in more and more fields. Signed network has a good effect in the process of representation and display. As an important part of network analysis, fuzzy community detection plays an increasingly important role in analyzing and visualizing the real world. Fuzzy community detection helps to detect nodes that belong to some communities but are still closely related to other communities. These nodes are helpful for mining information from the network more realistically. However, there is little research in this field. This paper proposes a fuzzy community detection algorithm based on pointer and adjacency list. The model adopts a new ICALF network data structure, which can achieve the effect of storing community partition structure and membership value between community and node at the same time, with low time complexity and storage space. Experiments on real networks verify the correctness of the method, and prove that the method is suitable for large-scale network applications.

Most multi-label learning (MLL) techniques perform classification by analyzing only the physical features of the data, which means they are unable to consider high-level features, such as structural and topological ones. Consequently, they have trouble to detect the semantic meaning of the data (e.g., formation pattern). To handle this problem, a high-level framework has been recently proposed to the MLL task, in which the high-level features are extracted using the analysis of complex network measures. In this paper, we extend that work by evaluating different combinations of four complex networks measures, namely clustering coefficient, assortativity, average degree and average path length. Experiments conducted over seven real-world data sets showed that the low-level techniques often can have their predictive performance improved after being combined with high-level ones, and also demonstrated that there is no a unique measure that provides the best results, i.e., different problems may ask for different network properties in order to have their high-level patterns efficiently detected.

Network analysis is performed on a 14 species food web model of the ecosystem occupying a mudflat on a partly reclaimed island of the Sundarban mangrove ecosystem. The results demonstrate a dramatic difference between this heavily impacted mangrove ecosystem in its modes of primary and secondary production and its diminished role of detritus vis-a-vis its less disturbed counterparts. Unlike most benthic mangrove systems, the Sundarban bottom community receives a large contribution from the phytoplankton populations. In this system herbivory and detritivory are virtually equal, in contrast to the usual herbivory:detritivory ratio of 1:5. Anthropogenic impacts have changed the physiography of this system so as to increase the relative importance of zooplankton and meiobenthos as herbivores. Although a slight degree of omnivory is exhibited by the populations of larger organisms, all flows of each integer of trophic length into a food chain may be aggregated that represents the underlying trophic status of the starting food web. Only a small number of pathways of recycle can be identified (31), and the Finn cycling index for this system is quite low (8.4%). Litterfall comprises only 16% of the total system input, which is very little in comparison with most mangrove systems. Pathway redundancy is rather high in this ecosystem, indicating that the surviving system is probably highly resilient to further perturbations, as one might expect for a highly impacted system.

This review paper provides an overview of complexity-based analysis techniques in biomedical image analysis, examining their theoretical foundations, computational methodologies, and practical applications across various medical imaging modalities. Through a synthesis of relevant literature, we explore the utility of complexity-based metrics such as fractal dimension, entropy measures, and network analysis in characterizing the complexity of biomedical images (e.g. magnetic resonance imaging (MRI), computed tomography (CT) scans, X-ray images). Additionally, we discuss the clinical implications of complexity-based analysis in areas such as cancer detection, neuroimaging, and cardiovascular imaging, highlighting its potential to improve diagnostic accuracy, prognostic assessment, and treatment outcomes. The review concludes that complexity-based analysis significantly enhances the interpretability and diagnostic power of biomedical imaging, paving the way for more personalized and precise medical care. By elucidating the role of complexity-based analysis in biomedical image analysis, this review aims to provide insights into current trends, challenges, and future directions in this rapidly evolving field.

Modern methods for network analytics provide an opportunity to revisit preconceived notions in the classification of diseases as “clusters of symptoms”. Curated collections which were subsequently modified, like the Diagnostic and Statistical Manuals of Mental Disorders “DSM-IV” and the most recent addition, DSM-5 allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM-5), potentially allows investigating if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV.

In this contribution, we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is based on the identification of “triangles” in the network; these triads are the building block of feedback loops that can exist between groups of symptoms. We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.

The Web of Data (WoD) is an Internet-based network of data resources and their relations. It has recently taken flight and combines over a hundred interlinked data sources with more than 15 billion edges. A consequence of this recent success is that a paradigm shift has taken place: up to now the Web of Data could be studied, searched and maintained like a classical database; nowadays it has turned into a Complex System and needs to be studied as such. In this paper, we introduce the Web of Data as a challenging object of study and provide initial results on two network scales: the pure data-layer, and the global connection between groups data items. In this analysis, we show that the "official" abstract representation of the WoD does not fit the real distribution we derive from the lower scale. As interesting as these results are, bigger challenges for analysis await in the form of the highly dynamic character of the WoD, and the typed, and implicit, character of the edges which is, to the best of our knowledge, hitherto unstudied.

In a large software project, the number of classes, and the dependencies between them, generally increase as software evolves. The size and scale of the system often makes it difficult to easily identify the important components in a particular software product. To address this problem, we model software as a network, where the classes are the vertices in the network and the dependencies are the edges, and apply K-core decomposition to identify a core subset of vertices as potentially important classes. We study three open source Java projects over a 10-year period and demonstrate, using different metrics, that the K-core decomposition of the network can help us identify the key classes of the corresponding software. Specifically, we show that the vertices with the highest core number represent the important classes and demonstrate that the core-numbers of classes with similar functionalities evolve at similar trends.

Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-specific differences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scientific success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adopt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages.

A network analysis of the structure of verbal communications in one of the most popular Russian novels of the Soviet era The Master and Margarita by M. A. Bulgakov has been carried out. The structure of the novel is complex, i.e. there is “a story within a story”. Moreover, the real-world-characters and the other-world-characters are interacting in the novel. This complex and unusual composition makes the novel especially attractive for a network analysis. In our study, only paired verbal communications (conversations) between explicitly present and acting characters have been taken into account; frontal communications, monologues, off-stage characters as well as expected connections between characters have not been taken into account. Based on a character pair verbal communication matrix, a graph has been constructed, the vertices of which are the characters of the novel, while the edges correspond to the connections between them. Taking only paired verbal communications into account leads to the result that the character network can be described by an ordinary, rather than a directed graph. Since the activity of the conversations was out of our intended scope, the edges have been given no weights. The largest connected component of the graph consists of 76 characters. Centralities, such as degree, betweenness, closeness, eigenvector, and assortativity coefficient were computed to characterize the network. The assortativity coefficient of the network under consideration is negative $-0.133$, i.e. the network does not demonstrate the properties of a social network. The structure of the communities in the network was also analyzed. In addition to the obvious large communities — the characters from the Yershalaim part of the novel and the characters of the Moscow part — the analysis also revealed a fine structure in the Moscow component. Using the analysis of centralities, a group of main characters has been detected. The central characters of the novel are Koroviev, Margarita, Bezdomny, Woland, Behemoth, Azazello, Bosoi, Warenukha, Master, and Levi Matthew.

Gene regulation in eukaryotes involves a complex interplay between the proximal promoter and distal genomic elements (such as enhancers) which work in concert to drive precise spatio-temporal gene expression. The experimental localization and characterization of gene regulatory elements is a very complex and resource-intensive process. The computational identification of regulatory regions that confer spatiotemporally specific tissue-restricted expression of a gene is thus an important challenge for computational biology. One of the most popular strategies for enhancer localization from DNA sequence is the use of conservation-based prefiltering and more recently, the use of canonical (transcription factor motifs) or de novo tissue-specific sequence motifs. However, there is an ongoing effort in the computational biology community to further improve the fidelity of enhancer predictions from sequence data by integrating other, complementary genomic modalities.

In this work, we propose a framework that complements existing methodologies for prospective enhancer identification. The methods in this work are derived from two key insights: (i) that chromatin modification signatures can discriminate proximal and distally located regulatory regions and (ii) the notion of promoter-enhancer cross-talk (as assayed in 3C/5C experiments) might have implications in the search for regulatory sequences that co-operate with the promoter to yield tissue-restricted, gene-specific expression.

Microbialites and microbial mats are complex communities with high phylogenetic diversity. These communities are mostly composed of bacteria and archaea, which are the earliest living forms on Earth and relevant to biogeochemical evolution. In this study, we identified the shared metabolic pathways for uptake of inorganic C and N in microbial mats and microbialites based on metagenomic data sets. An in silico analysis for autotrophic pathways was used to trace the paths of C and N to the system, following an elementary flux modes (EFM) approach, resulting in a stoichiometric model. The fragility was analyzed by the minimal cut sets method. We found four relevant pathways for the incorporation of CO₂ (Calvin cycle, reverse tricarboxylic acid cycle, reductive acetyl-CoA pathway, and dicarboxylate/4-hydroxybutyrate cycle), some of them present only in archaea, while nitrogen fixation was the most important source of N to the system. The metabolic potential to incorporate nitrate to biomass was also relevant. The fragility of the network was low, suggesting a high redundancy of the autotrophic pathways due to their broad metabolic diversity, and highlighting the relevance of reducing power source. This analysis suggests that microbial mats and microbialites are “metabolic pumps” for the incorporation of inorganic gases and formation of organic matter.

Understanding technology evolution through periodic landscaping is an important stage of strategic planning in R&D management. In fields like that of healthcare, where initial R&D investment is huge and good medical products serve patients better, activities of periodic landscaping become crucial for planning. Approximately 5% of the world’s population has hearing disabilities. Current hearing aid products meet less than 10% of the global needs. Patent data and classifications on cochlear implants from 1977–2010 show the technology evolution in the area of such an implant. We attempt to highlight emergence and disappearance of patent classes over a period of time indicating changes in growth of cochlear implant technologies. Using network analysis technique we explore and capture the technology evolution in patent classes by showing what emerged or disappeared over time. Dominant classes are identified. The sporadic influence of university research in cochlear implants is also discussed.