27 papers · ranked by Valyu relevance
Wei Ye, Jiayi Yang, Sourav Medya, Ambuj K. Singh
—Graph neural networks (GNNs) often assume strong homophily in graphs, seldom considering heterophily which means connected nodes tend to have different class labels and dissimilar features. In real-world scenarios, graphs may have nodes that exhibit both homophily and heterophily. Failing to generalize to this setting…
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu + 1 more
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to…
Saiful Islam, Md. Nahid Hasan, Pitambar Khanra
Classification Authors: ['Saiful Islam' 'Md. Nahid Hasan' 'Pitambar Khanra'] The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders…
Jinyin Chen, Haiyang Xiong, Haibin Zheng, Dunjie Zhang + 3 more
'Mingwei Jia' 'Yi Liu'] Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complexity for robustness, such as adversarial training, whereas others may trade off clean example performance, such as…
Masoud Kargar, Nasim Jelodari, Alireza Assadzadeh
graphs and graph convolutional networks for high-level feature extraction Authors: ['Masoud Kargar' 'Nasim Jelodari' 'Alireza Assadzadeh'] Graphs, comprising nodes and edges, visually depict relationships and structures, posing challenges in extracting high-level features due to their intricate connections Multiple…
Xiaoyu Zhang, Wenchuan Yang, Jiawei Feng, Bitao Dai + 2 more
'Xin Lü'] Abstract—Identifying structures in common forms the basis for networked systems design and optimization. However, real structures represented by graphs are often of varying sizes, leading to the low accuracy of traditional graph classification methods. These graphs are called cross-scale graphs. To overcome…
Ali Deeb, Abdalrahman Ibrahim, Mohamed Salem, Joachim Pichler + 6 more
'Sergii Tkachov' 'Anjeza Karaj' 'Fadi Al Machot' 'Kyamakya Kyandoghere' 'Marco Lanuzza' 'Lionel Trojman'] Analog mixed-signal (AMS) verification is one of the essential tasks in the development process of modern systems-on-chip (SoC). Most parts of the AMS verification flow are already automated, except for stimuli…
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
Lida Kanari, Stanislav Schmidt, Francesco Casalegno, Emilie Delattre + 7 more
The shape of neuronal morphologies plays a critical role in determining their dynamical properties and the functionality of the brain. With an abundance of neuronal morphology reconstructions, a robust definition of cell types is important to understand their role in brain functionality. However, an objective…
Anthony Gillioz, Kaspar Riesen
Despite great efforts done in research in the last decades, the classification of general graphs, i.e., graphs with unconstrained labeling and structure, remains a challenging task. Due to the inherent relational structure of graphs it is difficult, or even impossible, to apply standard pattern recognition methods to…
Authors not listed
Chemical reactions typically follow mechanistic templates and hence fall into a manageable number of clearly distinguishable classes that usually labeled by names of chemists who discovered or explored them. These ``named reactions'' form the core of reaction ontologies and are associated with specific synthetic…
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
Stephen K. Grady, Faisal N. Abu-Khzam, Ronald D. Hagan, Hesam Shams + 1 more
Deciding the size of a minimum dominating set is a classic NP-complete problem. It has found increasing utility as the basis for classifying vertices in networks derived from protein-protein, noncoding RNA, metabolic, and other biological interaction data. In this context it can be helpful, for example, to identify…
Serkan Ucer, Tansel Ozyer, Reda Alhajj
We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which…
Jiate Li, Binghui Wang
Perturbations with Deterministic Certification Authors: ['Jiate Li' 'Binghui Wang'] Graph neural networks (GNNs) achieve the state-of-the-art on graph-relevant tasks such as node and graph classification. However, recent works show GNNs are vulnerable to adversarial perturbations include the perturbation on edges…
Zhaoyang Wang, Xianghui Fu, Bo Deng, Yang Chen + 1 more
In algebraic topology, a k-dimensional simplex is defined as a convex polytope consisting of k + 1 vertices. If spatial dimensionality is not considered, it corresponds to the complete graph with k + 1 vertices in graph theory. The alternating sum of the number of simplices across dimensions yields a topological…
Diane Duroux, Kristel Van Steen
Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these…
Daniel M. Gonçalves, André Patrício, Rafael S. Costa, Rui Henriques
The growing availability and complexity of omics data have driven the development of specialized algorithms for modeling molecular systems. Although graph-based learning methods effectively represent biological interactions, they often neglect the statistical information embedded in node and edge annotations. To…
Matt I. B. Oddo, Ryan Smith, Stephen Kobourov, Tamara Munzner
—We investigate tasks that can be accomplished with unlabelled graphs, where nodes do not have persistent or semantically meaningful labels. New techniques to visualize these graphs have been proposed, but more understanding of unlabelled graph tasks is required before they can be adequately evaluated. Some tasks apply…
Srijani Bagchi, Anasua Sarkar, Ujjwal Maulik
In times like this, it is imperative to be cautious about the effects of drugs or vaccination doses on patients who are already suffering from other serious diseases. It’s not only the virus which can affect the body metabolisms, drugs to encounter the virus may also end up having unwanted negative effects. Therapeutic…
Kuan-Hao Chao, Pei-Wei Chen, Sanjit A. Seshia, Ben Langmead
A Wheeler graph represents a collection of strings in a way that is particularly easy to index and query. Such a graph is a practical choice for representing a graph-shaped pangenome, and it is the foundation for current graph-based pangenome indexes. However, there are no practical tools to visualize or to check…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Pulan Yu
Associative classification mining (ACM) integrating association rule mining and classification has become a significant tool for knowledge discovery, especially in the chemical domain. Its major advantage is providing high accuracy as well as chemically interpretable models. Additionally, it is able to find…
Jan Weinreich, Daniel Probst
In recent years, natural language processing approaches to machine learning, most prominently deep neural network-based transformers, have been extensively applied to molecular classification and regression tasks, including the prediction of pharmacokinetic and quantum-chemical properties. However, models based on deep…
Paul Kruse, Caroline Ring
This paper presents the treecompareR package for R, which provides tools for reproducible visualizations of data through the use of taxonomies. The package builds on developments from ggplot2 and ggtree to provide visualizations tailored for use with taxonomic classification data. Additionally, it provides tools that…
Daniel Probst
Last year, a preprint gained notoriety, proposing that a k-nearest neighbour classifier is able to outperform large-language models using compressed text as input and normalised compression distance (NCD) as a metric. In chemistry and biochemistry, molecules are often represented as strings, such as SMILES for small…