Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI networks with multiple functions and users in social or e-commerce networks exhibiting diverse interests. Tackling multi-label node classification (MLNC) on graphs has led to the development of various approaches. Some methods leverage graph neural networks (GNNs) to exploit label co-occurrence correlations, while others incorporate label embeddings to capture label proximity. However, these approaches fail to account for the intricate influences between labels in non-Euclidean graph data.To address this issue, we decompose the message passing process in GNNs into two operations: propagation and transformation. We then conduct a comprehensive analysis and quantification of the influence correlations between labels in each operation. Building on these insights, we propose a novel model, Label Influence Propagation (LIP). Specifically, we construct a label influence graph based on the integrated label correlations. Then, we propagate high-order influences through this graph, dynamically adjusting the learning process by amplifying labels with positive contributions and mitigating those with negative influence.Finally, our framework is evaluated on comprehensive benchmark datasets, consistently outperforming SOTA methods across various settings, demonstrating its effectiveness on MLNC tasks.
In IPM packages, efficiency of biological control is strongly dependent on the capacity of natural enemies to sustain in crops, whether these natural enemies have been released or promoted (through conservation methods) in the crops. To this purpose, the Hab’Alim project aims to identify and develop habitats providing feeding sources and shelters for the predators and the parasitoids of several greenhouse and outdoor crop pests. The possibility of using natural materials, supplying pollen, using companion plants, as well as foods supplements are studied both for their effects on pests and beneficial arthropod populations. Under laboratory and trials conditions, very encouraging results were obtained with materials studied as hemp as well as buckwheat husks, miscanthus and shetland wool, with pollens of Inula helenium, Lobularia maritima, Sorbaria sorbifolia, Typha angustifolia and Viburnum tinus, as well as prey mite Thyreophagus entomophagus.
Anl & auml;sslich des 175. Jahrestages der Ver & ouml;ffentlichung der ,,Musterbilder f & uuml;r Freunde des Sch & ouml;nen" wird an das Leben und Wirken von Friedlieb Ferdinand Runge und sein beeindruckendes Buch erinnert. Ein kurzer biografischer Abriss gibt Einblick in sein Leben, bevor seine chemischen Bilder n & auml;her vorgestellt und diskutiert werden. Eine Herstellungsvorschrift f & uuml;r diese chemischen Bilder und ein Hinweis auf deren Ver & auml;nderlichkeit komplettieren den Text. On the occasion of the 175th anniversary of the publication of "Musterbilder f & uuml;r Freunde des Sch & ouml;nen", the life and work of Friedlieb Ferdinand Runge and his impressive book are being commemorated. A short biographical sketch gives an insight into his life before his chemical pictures are presented and discussed in detail. The text is rounded off with instructions for making these chemical pictures and a note on their mutability.
In the early modern period, alchemical writings circulating under disguised author identities were part of a shadow economy of pseudepigraphical alchemical knowledge. By the end of the 16th century at the latest, the term Via Veritatis appeared as the title of alchemical, especially transmutatory, writings. For the period up to the 18th century, eighteen different versions were identified, which can be divided into five different text groups in terms of content. While the two older text groups (typus 1 and 2) criticize the state of contemporary alchemy and show the supposedly only true way to transmutation, the other text groups (typus 3 to 5) consist of a large number of systematically structured process instructions. However, a connection to the fictional person of Basilius Valentinus cannot be proven for all texts. The Via Veritatis texts of typus 1 and 2 show no obvious connections to Basilius Valentinus. For texts of typus 3 to 5, however, there appears to be reference to Basilius Valentinus in content and form. Based on the classification system proposed by Lawrence Principe for the corpus of Basilius Valentinus' writings, these can be assigned to sub-corpus C.
In complex systems like social media and financial transactions, diverse entities (users, groups, products) interact through a multitude of relationships (friendships, comments, purchases). These interactions can be represented by heterogeneous graphs (graphs with many node and edge types). In many real-world applications, these graphs may contain unusual patterns or anomalies. Detecting anomalies, both entity (node) level and interaction (edge) level anomalies, in these graphs are important, as their occurrence may have serious implications. Node-level anomalies may indicate abnormal behavior from a specific entity, such as unexpected activity that could suggest fraud. Edge-level anomalies may signify unusual interactions, like unexpected changes in interaction frequency or pattern, potentially indicating collaborative fraud like collusion.Unfortunately, existing graph neural network anomaly detection models focus only on homogeneous graphs and consider only node-level detection, rendering them incapable of harnessing the full complexity of heterogeneous graph data. To address this limitation, we present a new graph neural network model that capable of simultaneously detecting node-level and edge-level anomalies on heterogeneous graphs, by harnessing the rich information in the entities and relations. We develop our model as a type of graph autoencoder with a customized architecture design to enable the detection of node-level and edge-level anomalies simultaneously. Our graph neural network structure is scalable, facilitating its application in large real-world scenarios. Finally, our method outperforms previous anomaly detection methods in the experiments.
