View Of Network Research Articles

The Policy Potential of Monitoring Market Proximal Maritime Connectivity Over Time: The Case of Aotearoa New Zealand

AbstractA country’s maritime connectivity is integral to its international trade and regional economic growth. Advancements in technology and complex network theory have elevated maritime connectivity in regional transport policy agendas. This study introduces a novel conceptualisation of the maritime network called the market proximal network which better highlights connectivity concerns and opportunities that leverage a country’s current market position. By implementing a temporal view (using Time-Granularity Graphs) of the market proximal network, connectivity trends can be appreciated. The policy value of this approach is illustrated by the case of Aotearoa New Zealand, a maritime connectivity taker, between January 2018 and June 2021. Five maritime connectivity priorities are identified regarding the country’s gateways onto the major maritime highways. The potential policy value of the temporal view of the market proximal network is illustrated by identifying priorities that are actionable because they are based on the existing business cases that draw shipping companies to call in Aotearoa New Zealand.

Mathematical and neuro-mathematical connections activated by a teacher and his student in the geometric problems-solving: A view of networking of theories

The research goal is twofold: to articulate neuro-mathematics with the extended theory of mathematical connections that uses onto-semiotic approach tools and to explore the connections established by a teacher and his student when solving a problem about the volume of two boxes, one of toothpaste and the other of tomato. This research was developed in two stages: the theories were articulated assuming concordances and complementarities, highlighting the notion of connection, and a context of reflection was considered carried out in three phases where the participants were selected, participant observation was carried out in the classroom during solving a problem and then analyzing the data with the new tool to explore mathematical and neuro-mathematical connections. The findings present the mathematical connections established by the teacher and the student of meaning, feature, procedural, different representations (alternate, equivalent, and from a horizontal mathematization view), and part-whole, as well as neuro-mathematical connections of: recognition of terms and symbols; visual perception, spatial skills and motor coordination; association of mathematical concepts and formulas; intermediate calculations and unit conversion; solve operations step by step and understand the process; verification and conclusion, activated in the brain areas linked to each mathematical practice sequentially.

Service language and small talk: differentiating genuine from mundane in B2B contexts

PurposeThis study examines the role of genuine small talk in business-to-business (B2B) communication through the lens of service language. By exploring and distinguishing genuine and mundane small talk, the research elucidates the impact of sincere, empathetic interactions on value co-creation and relational dynamics within the B2B contexts.Design/methodology/approachEmploying an abductive qualitative research strategy, we conducted semi-structured interviews with 25 B2B professionals from Australian and Indonesian multinational corporations. Data analysis involved descriptive coding and thematic analysis, supplemented by network view strategies to visualize interrelations within the dataset.FindingsThe study finds that genuine small talk, characterized by sincere and contextually relevant exchanges, enhances relational enrichment, customer satisfaction and long-term loyalty in B2B interactions. Participants highlighted the importance of empathetic communication and personalized conversations in building trust and rapport. Conversely, mundane small talk, marked by superficiality, was found to erode trust and diminish the quality of client relationships. These insights underscore the critical importance of integrating genuine small talk into communication strategies to foster enduring business relationships and enhance overall service effectiveness.Originality/valueThis study contributes to the service literature by exploring the notion of genuine and mundane small talk in B2B communication, grounding them in service language perspective. It provides novel insights into the verbal and non-verbal elements of genuine small talk, offering practical implications for enhancing B2B negotiation strategies.

Structural alterations in a rumination-related network in patients with major depressive disorder

Rumination is a common symptom in major depressive disorder (MDD). Previous work has connected individual differences in rumination to structural properties in various brain regions. Some of these, such as the dorsolateral prefrontal cortex (dlPFC), have also been highlighted as being altered in MDD, suggesting a connection between structural changes and ruminative symptoms. Although informative, such localised relations have limitations in the context of a network view of the brain. To further investigate rumination-related structural changes in depression, and to situate these within potential functional networks, we acquired T1-weighted structural MRI data from patients with MDD (n=32) and controls (n=69). Rumination was measured with the Rumination Response Scale. Surface-based, whole-brain analysis of cortical grey-matter identified group differences in the dlPFC that were, however, not related to rumination. Instead, rumination was correlated with grey-matter properties in the right precuneus. Using normative functional connectivity analysis on an independent sample (n = 100), we show these two regions to be interconnected. Further developing a network-based perspective, it was shown that the rumination-related precuneus region is connected with networks associated with processes such as executive function, autobiographical memory, and visual perception. Notably, these processes have been connected to rumination. These results suggest that rumination in depression may be linked to focal structural changes. The effects of these focal changes on rumination may then be connected to their influence on distributed functional networks.

Advancing the complex adaptive systems approach to enterprise risk management with quantified risk networks (QRNs)

Business enterprises are complex adaptive systems (CAS) subject to fragility caused by the non-linear effects of uncertain risk events. The regional bank collapses in the United States are a case in point. Recent papers have highlighted a shortcoming of the traditional risk management process, which focuses on compliance but considers neither the interdependence between complex risks nor the mechanism of their non-linear impact on the organization. A new perspective on Enterprise Risk Management (ERM) has instead called for a shift of mindset from mitigating risk to building resilience by honing a CAS view to contextualize, assess, and manage complex risks. But the building blocks needed for a CAS representation of ERM have not yet been systematically developed. Specifically, we are missing a typological inventory of risks and a mapping of risks onto the general structure of an enterprise. In this paper, we build an industry-agnostic inventory of plausible risk factors using information extraction on large scale text data. Additionally, we develop an understanding of risk-function and function-function interdependencies through a survey of top business managers. The result is a novel complex network view of enterprise risk called a Quantified Risk Network (QRN) that displays small-world properties and highlights internal company functions central to non-linear risk propagation mechanisms within the enterprise. The QRN draws attention to vulnerabilities in the enterprise structure such as risk-function connections measured by Edge Betweenness centrality. The generic QRN developed herein is a proof of concept and we advocate that enterprises build their own company-specific QRNs to identify highly connected and central functions in their company structure that could lead to cascading failure when specific risks arise. QRNs can contribute to the objective of building enterprise resilience.

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

The "limbic network," comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI.

Resting-state functional magnetic resonance imaging (fMRI) investigations have provided a view of the default network (DN) as composed of a specific set of frontal, parietal, and temporal cortical regions. This spatial topography is typically defined with reference to an influential network parcellation scheme that designated the DN as one of seven large-scale networks (Yeo et al., 2011). However, the precise functional organization of the DN is still under debate, with studies arguing for varying subnetwork configurations and the inclusion of subcortical regions. In this vein, the so-called limbic network-defined as a distinct large-scale network comprising the bilateral temporal poles, ventral anterior temporal lobes, and orbitofrontal cortex-is of particular interest. A large multi-modal and multi-species literature on the anatomical, functional, and cognitive properties of these regions suggests a close relationship to the DN. Notably, these regions have poor signal quality with conventional fMRI acquisition, likely obscuring their network affiliation in most studies. Here, we leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage, including orbitofrontal and anterior temporal regions, to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the DN. Consistent with our hypotheses, our results support the inclusion of the majority of the orbitofrontal and anterior temporal cortex as part of the DN and reveal significant heterogeneity in their functional connectivity. We observed that left-lateralized regions within the temporal poles and ventral anterior temporal lobes, as well as medial orbitofrontal regions, exhibited the greatest resting-state functional connectivity with the DN, with heterogeneity across DN subnetworks. Overall, our findings suggest that, rather than being a functionally distinct network, the orbitofrontal and anterior temporal regions comprise part of a larger, extended default network.

Siamese Tracking Network with Multi-attention Mechanism

Object trackers based on Siamese networks view tracking as a similarity-matching process. However, the correlation operation operates as a local linear matching process, limiting the tracker’s ability to capture the intricate nonlinear relationship between the template and search region branches. Moreover, most trackers don’t update the template and often use the first frame of an image as the initial template, which will easily lead to poor tracking performance of the algorithm when facing instances of deformation, scale variation, and occlusion of the tracking target. To this end, we propose a Simases tracking network with a multi-attention mechanism, including a template branch and a search branch. To adapt to changes in target appearance, we integrate dynamic templates and multi-attention mechanisms in the template branch to obtain more effective feature representation by fusing the features of initial templates and dynamic templates. To enhance the robustness of the tracking model, we utilize a multi-attention mechanism in the search branch that shares weights with the template branch to obtain multi-scale feature representation by fusing search region features at different scales. In addition, we design a lightweight and simple feature fusion mechanism, in which the Transformer encoder structure is utilized to fuse the information of the template area and search area, and the dynamic template is updated online based on confidence. Experimental results on publicly tracking datasets show that the proposed method achieves competitive results compared to several state-of-the-art trackers.

Interconnectedness between Islamic and conventional banks: a multilayer network view

PurposeThis study aims to examine information (stock return, volatility and extreme risk) spillovers and interconnectedness within dual-banking systems.Design/methodology/approachUsing multilayer information spillover networks, this paper conduct a deep analysis of contagion dynamics among 24 Islamic and 46 conventional banks from 2006 to 2022.FindingsThe findings show the network’s rapid response to financial shocks. Through cross-sector analysis, this paper identify information spillovers between and within Islamic and conventional banking systems. Furthermore, this research illustrates distinct roles played by Islamic and conventional banks within the multilayer network structure, contingent upon the nature of the financial shock.Practical implicationsUnderstanding the differential roles of Islamic and conventional banks in information transmission can aid policymakers and financial institutions in devising more effective risk management strategies, thereby enhancing financial stability within dual-banking systems.Originality/valueThis study contributes to the literature by emphasizing the necessity of examining contagion mechanisms beyond traditional single-layer network structures, shedding light on the shadow dynamics of information transmission in dual-banking systems.

A Survey of Controller Placement Problem in SDN-IoT Network

AbstractThe Internet of Things (IoT) refers to the billions of intelligent physical devices connected to the Internet for collecting and sharing data. However, implementing IoT in large-scale industrial applications presents numerous challenges, including network management and scalability. These challenges encompass: complex network management tasks that are increasingly difficult to maintain, increased network resource usage, mobility, and high energy consumption. Software-defined networking (SDN) addresses these limitations by enforcing centralized control of all devices and leveraging a global network view. SDN is a networking paradigm that separates the control plane from the data plane, allowing managers to centralize the control of the network infrastructure. For large networks, such as IoT networks, multiple controllers are needed to manage the network efficiently. The Controller Placement Problem (CPP) involves the challenge of deploying the optimal number of controllers in a network while satisfying specific performance requirements such as latency, load balancing, and computation time. This paper provides an overview of recent research efforts addressing CPP issues in the SDN-IoT domain.

ScHyper: reconstructing cell-cell communication through hypergraph neural networks.

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand-receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell-cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.

Bta-miR-200a Regulates Milk Fat Biosynthesis by Targeting IRS2 to Inhibit the PI3K/Akt Signal Pathway in Bovine Mammary Epithelial Cells.

Milk fat synthesis has garnered significant attention due to its influence on the quality of milk. Recently, an increasing amount of proofs have elucidated that microRNAs (miRNAs) are important post-transcriptional factor involved in regulating gene expression and play a significant role in milk fat synthesis. MiR-200a was differentially expressed in the mammary gland tissue of dairy cows during different lactation periods, which indicated that miR-200a was a candidate miRNA involved in regulating milk fat synthesis. In our research, we investigated the potential function of miR-200a in regulating milk fat biosynthesis in bovine mammary epithelial cells (BMECs). We discovered that miR-200a inhibited cellular triacylglycerol (TAG) synthesis and suppressed lipid droplet formation; at the same time, miR-200a overexpression suppressed the mRNA and protein expression of milk fat metabolism-related genes, such as fatty acid synthase (FASN), peroxisome proliferator-activated receptor gamma (PPARγ), sterol regulatory element-binding protein 1 (SREBP1), CCAAT enhancer binding protein alpha (CEBPα), etc. However, knocking down miR-200a displayed the opposite results. We uncovered that insulin receptor substrate 2 (IRS2) was a candidate target gene of miR-200a through the bioinformatics online program TargetScan. Subsequently, it was confirmed that miR-200a directly targeted the 3'-untranslated region (3'-UTR) of IRS2 via real-time fluorescence quantitative PCR (RT-qPCR), western blot analysis, and dual-luciferase reporter gene assay. Additionally, IRS2 knockdown in BMECs has similar effects to miR-200a overexpression. Our research set up the mechanism by which miR-200a interacted with IRS2 and discovered that miR-200a targeted IRS2 and modulated the activity of the PI3K/Akt signaling pathway, thereby taking part in regulating milk fat synthesis in BMECs. Our research results provided valuable information on the molecular mechanisms for enhancing milk quality from the view of miRNA-mRNA regulatory networks.

Facilitating the circular economy: insights from novel supply network actors

PurposeThis research studies circularity brokers, the economic actors facilitating the transition of companies towards circular operations. The purpose of this paper is to extend the understanding of “circular brokerage”, deriving from the concept of brokerage existing in network theory and applied to the specific context of the circular supply network. Recent papers have been devoted to particular facets of circular brokerage, with studies dedicated to circular incubators or platforms. This study proposes how these new actors can have a significant role in facilitating circular economy in general.Design/methodology/approachThe methodology is qualitative using 17 semi-directed interviews with circularity brokers. The theoretical approach draws on the network view and builds on recent developments of the theory in the context of circular economy.FindingsCircularity brokers support organisations and their networks, helping them to overcome the problems that stand in their way of adopting circular economy at different levels (micro, meso and macro). To do this, circularity brokers deploy a wide range of activities involving technical, methodological and relational skills. The study extends previous research by elaborating on the relationship between the position these actors occupy within their network and the benefits they can bring in terms of circularity.Originality/valueThis research provides evidence on the role of circularity brokers and moves the state of knowledge from a specific sector or cases to other sectors and a broader role beyond network position specifics. Network theory, with the constructs of broker exchanges and the influence of embeddedness, is used to develop theoretical implications.

Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways.

Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.

Intra-chloroplast proteases: A holistic network view of chloroplast proteolysis.

Different proteases and peptidases are present within chloroplasts and nonphotosynthetic plastids to process precursor proteins and to degrade cleaved chloroplast transit peptides and damaged, misfolded, or otherwise unwanted proteins. Collectively, these proteases and peptidases form a proteolysis network, with complementary activities and hierarchies, and build-in redundancies. Furthermore, this network is distributed across the different intra-chloroplast compartments (lumen, thylakoid, stroma, envelope). The challenge is to determine the contributions of each peptidase (system) to this network in chloroplasts and nonphotosynthetic plastids. This will require an understanding of substrate recognition mechanisms, degrons, substrate, and product size limitations, as well as the capacity and degradation kinetics of each protease. Multiple extra-plastidial degradation pathways complement these intra-chloroplast proteases. This review summarizes our current understanding of these intra-chloroplast proteases in Arabidopsis and crop plants with an emphasis on considerations for building a qualitative and quantitative network view.

Influence of binder content on gas-water two-phase flow and displacement phase diagram in the gas diffusion layer of PEMFC: A pore network view

Understanding the gas-water flow dynamics in the gas diffusion layer (GDL) is one of the keys to improving the proton exchange membrane fuel cell's (PEMFC) overall performance and avoiding water flooding. Yet, the relationship between the two-phase flow and micro-scale GDL structures (including binder, PTFE, and fiber skeleton) is not well understood. In this study, three-dimensional GDL structures with different fractions of binder contents (φb=0%∼50%) and a fixed porosity of 75.6 % are confidently reconstructed based on high-resolution images from the confocal microscope, implying that the addition of binder will increase the number of large pores and cause a bimodal throat size distribution through morphology analysis. Thereafter, drainage simulations are performed under a wide range of capillary number (−9 < log Ca <0) and viscosity ratio (−3 < log M < 2) by the pore network model (PNM), which robustly predicts the displacement phase diagram of viscous fingering, capillary fingering, stable displacement, and transitional regime. In the viscous fingering and stable displacement regime, the nonwetting phase saturation (Snw) is nearly the same for different φb, implying the fluid invading pathway does not change much with the addition of binder. However, in the capillary fingering regime (refer to the actual operating conditions), Snw increases rapidly with φbat the breakthrough time and its transient value continues to increase until the steady state, which may be attributed to the increased fractions of pores above the capillary threshold attributed by the bimodal throat size distribution. In addition, the effective water permeability increases sharply with φb, whereas the gas permeability remains almost constant, which means such pore structure alteration by adding the binder is beneficial to water disposal in PEMFC. This research provides insights into delineating the effect of pore and throat size distribution alteration by adding binder on two-phase dynamics in GDL.

The 2023 Merger Guidelines: Law, Fact, and Method

The final version of the 2023 Merger Guidelines, which were issued in December 2023, is a vast improvement over an earlier draft—which indicates that the Agencies took the many comments that they received on a draft very seriously. These Guidelines break some new ground that older Guidelines did not address, and make many positive contributions, which this paper spells out. They are also excessively nostalgic for a past era, however, and this may explain their propen sity to treat empirical questions as issues of law: This is one way to insulate these Guidelines from further revision. The excessive reliance on one decision, Brown Shoe, is unfortunate—particularly since that decision has been so often repudiated, even by the Supreme Court itself. This paper pays particular attention to: the Guidelines’ treatment of structural triggers and direct measures of competitive effects; their aggressive position on potential competition mergers; their willingness to weigh a “trend” toward concentration as a factor; and their treatment of serial acquisitions. The Guidelines include a welcome new section on mergers involving multi-sided networks, although their view of networks is too one-sided; and the Guidelines also contain an expanded section on mergers with harmful effects on suppliers—including labor. The Guidelines’ treatment of market definition is likely to lead to underenforcement because they define markets too broadly. Finally, the Guidelines could have made better use of recent retrospective studies—many of which would have provided further support for the substantive positions that the Guidelines take.

Elucidating the callus-to-shoot-forming mechanism in Capsicum annuum ‘Dempsey’ through comparative transcriptome analyses

BackgroundThe formation of shoots plays a pivotal role in plant organogenesis and productivity. Despite its significance, the underlying molecular mechanism of de novo regeneration has not been extensively elucidated in Capsicum annuum ‘Dempsey’, a bell pepper cultivar. To address this, we performed a comparative transcriptome analysis focusing on the differential expression in C. annuum ‘Dempsey’ shoot, callus, and leaf tissue. We further investigated phytohormone-related biological processes and their interacting genes in the C. annuum ‘Dempsey’ transcriptome based on comparative transcriptomic analysis across five species.ResultsWe provided a comprehensive view of the gene networks regulating shoot formation on the callus, revealing a strong involvement of hypoxia responses and oxidative stress. Our comparative transcriptome analysis revealed a significant conservation in the increase of gene expression patterns related to auxin and defense mechanisms in both callus and shoot tissues. Consequently, hypoxia response and defense mechanism emerged as critical regulators in callus and shoot formation in C. annuum ‘Dempsey’. Current transcriptome data also indicated a substantial decline in gene expression linked to photosynthesis within regenerative tissues, implying a deactivation of the regulatory system governing photosynthesis in C. annuum ‘Dempsey’.ConclusionCoupled with defense mechanisms, we thus considered spatial redistribution of auxin to play a critical role in the shoot morphogenesis via primordia outgrowth. Our findings shed light on shoot formation mechanisms in C. annuum ‘Dempsey’ explants, important information for regeneration programs, and have broader implications for precise molecular breeding in recalcitrant crops.

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.