Negative Edge Research Articles

Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems

BackgroundDisease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems.MethodsIn this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state.ResultsThe proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called “dark genes” that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis.ConclusionsThe proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

SiReN: Sign-Aware Recommendation Using Graph Neural Networks.

In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new Sign-aware Recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users' preferences, which is split into two edge-disjoint graphs with positive and negative edges each; 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multilayer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings; and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.

Adorno's Problematic Entanglement with Blackness

Black aesthetics and Adornian aesthetics both articulate and embody what Hortense Spillers calls the “critical edge” of culture and art, and there is a recent history of Black aestheticians engaging creatively with Adorno. Today, however, any constructive dialogue between these aesthetic traditions depends on whether Adorno's aesthetics can be decoupled from the antiblack racism in the genealogy of Western aesthetics and whether Adorno's negative dialectics can be reconciled to the transformative force of art, as that force is as central in Black aesthetics as is the negative dialectics of art. Critically focusing on Adorno's discussion of “the ideal of blackness” in Aesthetic Theory, this article will argue that such decoupling remains incomplete, that he resolutely did not believe in the transformative force of art, and that these issues are connected, at least in Adorno's case. This article simultaneously demonstrates how Black thinkers—Frederick Douglass, W. E. B. Du Bois, Angela Y. Davis, Fred Moten, Fumi Okiji, and Spillers—have long offered exemplary accounts of art that combine its negative dialectics, critical edge, and transformative force while defying the antiblack racism in Western aesthetics.

Where the small things are: Modelling edge effects on mouse lemur population density and distribution in northwestern Madagascar.

Edge effects result from the penetration to varying depths and intensities, of abiotic and biotic conditions from the surrounding non-forest matrix into the forest interior. Although 70% of the world's forests are within 1 km of a forest edge, making edge effects a dominant feature of most forest habitats, there are few empirical data on inter-site differences in edge responses in primates. We used spatially explicit capture-recapture (SECR) models to determine spatial patterns of density for two species of mouse lemurs (Microcebus murinus and Microcebus ravelobensis) in two forest landscapes in northwestern Madagascar. The goal of our study was to determine if mouse lemurs displayed spatially variable responses to edge effects. We trapped animals using Sherman live traps in the Mariarano Classified Forest (MCF) and in the Ambanjabe Forest Fragment Site (AFFS) site within Ankarafantsika National Park. We trapped 126 M. murinus and 79 M. ravelobensis at MCF and 78 M. murinus and 308 M. ravelobensis at AFFS. For M. murinus, our top model predicted a positive edge response, where density increased towards edge habitats. In M. ravelobensis, our top model predicted a negative edge response, where density was lower near the forest edges and increased towards the forest interior. At regional and landscape-specific scales, SECR models estimated different density patterns between M. murinus and M. ravelobensis as a result of variation in edge distance. The spatial variability of our results using SECR models indicate the importance of studying the population ecology of primates at varying scales that are appropriate to the processes of interest. Our results lend further support to the theory that some lemurs exhibit a form of ecological flexibility in their responses to forest loss, forest fragmentation, and associated edge effects.

Effect of annealing temperature on bismuth vanadate nano thin films for solar cell applications

Bismuth vanadate (BiVO4) has been used as the photoanode electrodes in ferroelectric solar cells owing to its unique combination of properties: a narrow bandgap among ferroelectrics, economic viability, negative conduction band edge, and remarkable stability. The present work explores the fabrication and characterization of BiVO4 thin films prepared via spin coating, with a specific focus on elucidating the influence of the annealing temperature (400–550 °C) on their structural, optical, and photovoltaic properties. From X-ray diffraction (XRD) analysis, it is observed that higher annealing temperatures have promoted the formation of larger grains, enhanced crystallinity, and induced a preferred crystal orientation characteristic of the monoclinic scheelite structure. Tauc plot analysis shows the dependence of the optical band gap on the annealing temperature for BiVO4 thin films. The band gap values have decreased slightly from 2.50 eV at 400 °C to 2.44 eV at 550 °C. This is indicated by the slightly narrowed bandgap, which influences the structure of the material or defect states at higher annealing temperatures. A narrower bandgap allows for the absorption of lower-energy light, potentially improving the light absorption efficiency of BiVO4 thin films in photoanode applications. The influence of annealing temperature on BiVO₄ thin film solar cell performance was investigated further through the analysis of open-circuit voltage (Voc), short-circuit current (Isc) and power conversion efficiency.

Afforestation alters soil microbial community composition and reduces microbial network complexity in a karst region of Southwest China

AbstractSoil microbial community plays important roles in altering ecological processes and biogeochemical cycles in ecosystems. However, little is known about how afforestation influences the diversity, composition, and ecological network of soil microbial community in karst regions. In this study, soil samples were collected from one farmland (FL) and three afforestation lands including one bamboo forest (BA), one landscape tree planting forest (LAT), and one orange orchard (ORO) in a karst region of Southwest China. The bacterial and fungal communities were characterized using the high‐throughput sequencing approach, and soil properties including soil organic carbon, pH, soil water content, and soil total and available nutrient contents were measured under different land use treatments. Results showed that conversion from FL to BA and LAT significantly reduced the Shannon diversity of bacterial community. At the phylum level (top 10), genus level (top 30), and operational taxonomic units (OTUs) level, afforestation from FL resulted in significant changes in nine phyla, 24 genera, and 31.32% OTUs of bacterial community, and in three phyla, 13 genera, and 11.62% OTUs of fungal community. The number of nodes, number of negative edges, connectivity, average degree, and relative modularity of the microbial network under afforestation lands decreased by 9.33%–18.66%, 47.98%–72.75%, 0.45%–5.93%, 14.73%–22.29%, and 6.46%–23.50%, respectively, compared with FL. The soil organic carbon, total potassium and total phosphorus were identified as the key soil properties affecting the microbial community. Compared with LAT and ORO, the changes in bacterial and fungal communities under BA were more obvious because of the higher contents in soil organic carbon (13.48%) and total potassium (27.18%). In conclusion, afforestation significantly changed compositions and ecological network complexity of soil microbial community in karst regions, and conversion from FL to BA had stronger influences on the changes of soil microbial community than other afforestation lands in Southwest China. These findings provide the context necessary to evaluate the responses of soil microbial community to land use changes in karst regions.

Circular flows in mono‐directed signed graphs

AbstractIn this paper, the concept of circular ‐flow in a mono‐directed signed graph is introduced. That is a pair , where is an orientation on and satisfies that for each positive edge and for each negative edge , and the total in‐flow equals the total out‐flow at each vertex. This is the dual notion of circular colorings of signed graphs and is distinct from the concept of circular flows in bi‐directed graphs associated with signed graphs studied in the literature. We first explore the connection between circular ‐flows and modulo ‐orientations in signed graphs. Then we focus on the upper bounds for the circular flow indices of signed graphs in terms of the edge‐connectivity, where the circular flow index of a signed graph is the minimum value such that it admits a circular ‐flow. We prove that every 3‐edge‐connected signed graph admits a circular 6‐flow and every 4‐edge‐connected signed graph admits a circular 4‐flow. More generally, for , we show that every ‐edge‐connected signed graph admits a circular ‐flow, every ‐edge‐connected signed graph has a circular ‐flow with , and every ‐edge‐connected signed graph admits a circular ‐flow. Moreover, the ‐edge‐connectivity condition is shown to be sufficient for a signed Eulerian graph to admit a circular ‐flow, and applying this result to planar graphs, we conclude that every signed bipartite planar graph of negative girth at least admits a homomorphism to the negative even cycles .

The recovery of soil eukaryotic alpha and beta diversity after wetland restoration

Soil eukaryotes play an important role in regulating the ecological processes and ecosystem functioning. However, the recovery potential of soil eukaryotic diversity during wetland restoration is largely unknown. We compared the alpha and beta diversity of soil eukaryotes of farmlands and natural and restored wetlands to explore the underlying abiotic and biotic driving forces in the Sanjiang Plain, China. We found that there was no significant difference of the alpha diversity of soil eukaryotes, while the beta diversity of soil eukaryotes differed significantly between the three land use types, with the mean values in the restored wetlands in between those in the natural wetlands and farmlands. The composition of soil eukaryotic communities were less diverse in farmlands compared to restored and natural wetlands. Network property of soil eukaryotes community (positive: negative edges) increased from farmlands to restored wetlands to natural wetlands, indicating enhanced species positive: negative interactions during restoration. The structural equation modeling indicated that species positive: negative interactions and soil nutrients directly affected soil eukaryotic beta diversity. Soil pH and soil water content indirectly affected soil eukaryotic beta diversity by directly affecting species interactions. Our findings suggest that wetland restoration could change soil environment, strengthen microbial cooperation, and increase eukaryotic beta diversity. However, it may take a very long time to reach the original level of soil eukaryotic structure and diversity.

Multimodal prediction of student performance: A fusion of signed graph neural networks and large language models

In online education platforms, accurately predicting student performance is essential for timely dropout prevention and interventions for at-risk students. This task is made difficult by the prevalent use of Multiple-Choice Questions (MCQs) in learnersourcing platforms, where noise in student-generated content and the limitations of existing unsigned graph-based models, specifically their inability to distinguish the semantic meaning between correct and incorrect responses, hinder accurate performance predictions. To address these issues, we introduce the Large Language Model enhanced Signed Bipartite graph Contrastive Learning (LLM-SBCL) model—a novel Multimodal Model utilizing Signed Graph Neural Networks (SGNNs) and a Large Language Model (LLM). Our model uses a signed bipartite graph to represent students’ answers, with positive and negative edges denoting correct and incorrect responses, respectively. To mitigate noise impact, we apply contrastive learning to the signed graphs, combined with knowledge point embeddings from the LLM to further enhance our model’s predictive performance. Upon evaluating our model on five real-world datasets, it demonstrates superior accuracy and stability, exhibiting an average F1 improvement of 3.7% over the best baseline models.

The evolutionary prisoner’s dilemma game in continuous signed networks

Cooperative evolutionary games have been a focus in various subjects, but the discussion on signed networks is lacking, especially when players are assumed to have different degree of closeness with others, i.e. continuous signed network. To fill this gap, we introduce a negativity coefficient that responds to the effect of negative relationships and a new strategy imitation method that applies to continuous networks. The simulation results show the necessity of negative relations that promotes cooperation, and the increase of the influence of negative relations facilitates the generation of cooperative behaviors. The edge updating in adaptive networks promote cooperation but has little impact when negative edge influence is small. The experiments also prove that structural balance is helpful for increasing the share of cooperators in the evolution.

Similarities and Differences Between Study Addiction and Study Engagement and Work Addiction and Work Engagement: A Network Analysis

AbstractThis study aimed to investigate the relationships between symptoms of study addiction and their relationships with dimensions of study engagement. We used two samples in which study addiction was measured with the Bergen Study Addiction Scale and study engagement was measured with the Utrecht Work Engagement Scale for Students. The samples comprised Norwegian (n = 1107) and Polish (n = 776) students. The networks featured three clusters of nodes (two clusters of the study addiction symptoms and one cluster of the study engagement dimensions). Study addiction clusters were connected with study engagement cluster through positive edges between absorption and study addiction symptoms, negative edges which vigor shared with conflict and problems, and negative edge between dedication and mood modification. Among the symptoms of study addiction, problems, conflict, and withdrawal were the most central and salience, tolerance, and mood modification were the least central. Moreover, conflict was the most predictable and relapse was the least predictable symptom of study addiction. The results from the present study are similar to those of previous studies on work addiction and support the notion that study addiction may be a precursor to work addiction. Although based on cross-sectional data, the nuanced differences between work addiction and study addiction networks are discussed as they lay a foundation for further investigation of the potential differences in transition mechanisms from healthy engagement to compulsive behavior.

Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning.

Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.

Overgrazing on unmanaged grassland interfered with the restoration of adjacent grazing-banned grassland by affecting soil properties and microbial community.

A "grazing ban" policy has been implemented in some pastoral areas in China to fence degraded grasslands for restoration. However, fencing increased grazing pressures in unmanaged grasslands. Based on the mechanism of negative edge effect, we investigated whether overgrazing on unmanaged grassland interfered with the restoration of adjacent grazing-banned grassland by affecting soil properties and microbial community using a sample in Hulun Buir of Inner Mongolia, in order to optimize the "grazing ban" policy. Plant and soil were sampled in areas 30 m away from the fence in unmanaged grassland (UM) and in areas 30 m (adjacent to UM) and 30-60 m (not adjacent to UM) away from the fence in the grazing-banned grassland (F-30 m and F-60 m). The species richness and diversity of plant communities and the ASV number of fungal communities significantly decreased in F-30 m and UM, and the Simpson index of the bacterial community significantly decreased in F-30 m compared with F-60 m. The abundance of fungi involved in soil organic matter decomposition significantly decreased and the abundance of stress-resistant bacteria significantly increased, while the abundance of bacteria involved in litter decomposition significantly decreased in UM and F-30 m compared with F-60 m. The simplification of plant communities decreased in soil water and total organic carbon contents can explain the variations of soil microbial communities in both UM and F-30 m compared with F-60 m. The results of PLS-PM show that changes in plant community and soil microbial function guilds in UM may affect those in F-30 m by changing soil water and total organic carbon contents. These results indicate that overgrazing on unmanaged grassland interfered with the restoration of adjacent grazing-banned grassland by affecting soil properties and microbial community. The grazing-banned grasslands should be adjusted periodically in order to avoid negative edge effects.

Complete signed graphs with largest maximum or smallest minimum eigenvalue

In this paper, we deal with extremal eigenvalues of the adjacency matrices of complete signed graphs. The complete signed graphs with maximal index (i.e. the largest eigenvalue) with n vertices and m≤⌊n2/4⌋ negative edges have been already determined. We address the remaining case by characterizing those with m>⌊n2/4⌋ negative edges. We also identify the unique signed graph with maximal index among complete signed graphs whose negative edges induce a tree of diameter at least d for any given d. This extends a recent result by Li, Lin, and Meng [Discrete Math. 346 (2023), 113250] who established the same result for d=2. Finally, we prove that the smallest minimum eigenvalue of complete signed graphs with n vertices whose negative edges induce a tree is −n2−1−1+O(1n).

Contextualized Trajectory Parsing with Spatio-Temporal Graph

This work investigates how to automatically parse object trajectories in surveillance videos, which aims at jointly solving three subproblems: 1) spatial segmentation, 2) temporal tracking, and 3) object categorization. We present a novel representation spatiotemporal graph (ST-Graph) in which: 1) Graph nodes express the motion primitives, each representing a short sequence of small-size patches over consecutive images, and 2) every two neighbor nodes are linked with either a positive edge or a negative edge to describe their collaborative or exclusive relationship of belonging to the same object trajectory. Phrasing the trajectory parsing as a graph multicoloring problem, we propose a unified probabilistic formulation to integrate various types of context knowledge as informative priors. An efficient composite cluster sampling algorithm is employed in search of the optimal solution by exploiting both the collaborative and the exclusive relationships between nodes. The proposed framework is evaluated over challenging videos from public datasets, and results show that it can achieve state-of-the-art tracking accuracy.

Nutrient‐demanding and thermophilous plants dominate urban forest‐edge vegetation across temperate Europe

AbstractQuestionsForests are highly fragmented across the globe. For urban forests in particular, fragmentation increases the exposure to local warming caused by the urban heat island (UHI) effect. We here aim to quantify edge effects on herbaceous understorey vegetation in urban forests, and test whether these effects interact with forest structural complexity.LocationWe set up a pan‐European study at the continental scale including six urban forests in Zurich, Paris, Katowice, Brussels, Bremen, and Stockholm.MethodsWe recorded understorey plant communities from the edge towards the interior of urban forests. Within each urban forest, we studied edge‐to‐interior gradients in paired stands with differing forest structural complexity. Community composition was analysed based on species specialism, life form, light, nutrient, acidity and disturbance indicator values and species' thermal niches.ResultsWe found that herbaceous communities at urban forest edges supported more generalists and forbs but fewer ferns than in forests' interiors. A buffered summer microclimate proved crucial for the presence of fern species. The edge communities contained more thermophilous, disturbance‐tolerant, nutrient‐demanding and basiphilous plant species, a pattern strongly confirmed by corresponding edge‐to‐interior gradients in microclimate, soil and light conditions in the understorey. Additionally, plots with a lower canopy cover and higher light availability supported higher numbers of both generalists and forest specialists. Even though no significant interactions were found between the edge distance and forest structural complexity, opposing additive effects indicated that a dense canopy can be used to buffer negative edge effects.ConclusionThe urban environment poses a multifaceted filter on understorey plant communities which contributes to significant differences in community composition between urban forest edges and interiors. For urban biodiversity conservation and the buffering of edge effects, it will be key to maintain dense canopies near urban forest edges.

Source localization in signed networks with effective distance

While progress has been made in information source localization, it has overlooked the prevalent friend and adversarial relationships in social networks. This paper addresses this gap by focusing on source localization in signed network models. Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance, we propose an optimization method for observer selection. Additionally, by using the reverse propagation algorithm we present a method for information source localization in signed networks. Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization, and the higher the ratio of propagation rates between positive and negative edges, the more accurate the source localization becomes. Interestingly, this aligns with our observation that, in reality, the number of friends tends to be greater than the number of adversaries, and the likelihood of information propagation among friends is often higher than among adversaries. In addition, the source located at the periphery of the network is not easy to identify. Furthermore, our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization, compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Innovative method to solve the minimum spanning tree problem: The Dhouib-Matrix-MSTP (DM-MSTP)

The Minimum Spanning Tree problem aims to create a subset of a graph where all the vertices are connected with the minimum edge weights and with no cycle. In this field, an innovative method entitled Dhouib-Matrix-MSTP (DM-MSTP) is designed in this research work with a time complexity independently of the number of edges O(n*log(n)) where n is the number of vertices. DM-MSTP is a constructive algorithm based on a matrix navigation with two new lists (Min-Columns and MST-Path) in order to organize the steering flow. DM-MSTP is composed of four simple steps where the first and the fourth steps are repeated only once, whereas the second and third are reiterated (n-1). For more clarification, a step-by-step application of the proposed method is presented in details. Besides, the performance of DM-MSTP is proved through different examples from the literature including a graph with negative weighted edges and complete graphs from TSP-LIB. Moreover, with a simple modification (Min by Max) the DM-MSTP is tested on the Maximum (Largest) Spanning Tree Problem. Also, DM-MSTP is applied on eight case studies and compared to six methods developed in the literature. All these experimental results in the above different environments show that DM-MSTP can rapidly plan the shortest spanning tree with a stable performance and convivial representation of the optimal tree. Hence, DM-MSTP is developed under Python programming language using Matplotlib and Numpy standard libraries.

Characterizing signed mixed graphs with small eigenvalues

A mixed graph MG is obtained from a simple graph G by orienting an edge subset of G. A signed mixed graph is a mixed graph with arcs and edges signed + or −. The unit Eisenstein matrix (E-matrix for short) of a signed mixed graph was recently introduced by Wissing and van Dam [32]. This novel matrix is indexed by the vertices of the signed mixed graph, and the entry corresponding to a positive arc from u to v is equal to ω=1+i32 (and its symmetric entry is ω‾=1−i32); the entry corresponding to a negative arc is equal to −ω (and its symmetric entry is −ω‾); the entry corresponding to a positive edge is equal to 1; the entry corresponding to a negative edge is equal to −1; and 0 otherwise. In this paper, we study the spectral properties of this E-matrix. We characterize all the signed mixed graphs whose eigenvalues are contained in (−α,α) for α∈{2,3,2}.

Robust Avoidance of Edge-Localized Modes alongside Gradient Formation in the Negative Triangularity Tokamak Edge.

In a series of high performance diverted discharges on DIII-D, we demonstrate that strong negative triangularity (NT) shaping robustly suppresses all edge-localized mode (ELM) activity over a wide range of plasma conditions: ⟨n⟩=0.1-1.5×10^{20} m^{-3}, P_{aux}=0-15 MW, and |B_{t}|=1-2.2 T, corresponding to P_{loss}/P_{LH08}∼8. The full dataset is consistent with the theoretical prediction that magnetic shear in the NT edge inhibits access to ELMing H-mode regimes; all experimental pressure profiles are found to be at or below the infinite-n ballooning stability limit. Our present dataset also features edge pressure gradients in strong NT that are closer to an H-mode than a typical L-mode plasma, supporting the consideration of NT for reactor design.