28 papers · ranked by Valyu relevance
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer, Iakovos Evdaimon + 1 more
We introduce GraViti, a transformer-based graph-level variational autoencoder that maps entire graphs to compact latent vectors. This design produces a true graph-level latent space that supports smooth interpolation, property-guided search, and other downstream tasks beyond the constraints of node-level embeddings. On…
Akhil Pandey Akella
Dimensionality reduction techniques map data represented on higher dimensions onto lower dimensions with varying degrees of information loss. Graph dimensionality reduction techniques adopt the same principle of providing latent representations of the graph structure with minor adaptations to the output representations…
Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth + 3 more
'David Broneske' 'Gunter Saake' 'Robert Heyer'] Title: Abstract The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving…
Dongqi Fu, Jingrui He
Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of…
Sotirios Panagiotis Chytas, Rudrasis Chakraborty, Vikas Singh
Recent results show that modern Large Language Models (LLM) are indeed capable of understanding and answering questions about structured data such as graphs. This new paradigm can lead to solutions that require less supervision while, at the same time, providing a model that can generalize and answer questions beyond…
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…
Radoslav Dimitrov, Zeyang Zhao, Ralph Abboud, İsmail İlkan Ceylan
Graph neural networks iteratively compute representations of nodes of an input graph through a series of transformations in such a way that the learned graph function is isomorphism invariant on graphs, which makes the learned representations graph invariants. On the other hand, it is well-known that graph invariants…
Maryam Mohsenivatani, Samad Ali, Vismika Ranasinghe, Nandana Rajatheva + 1 more
'Nandana Rajatheva' 'Matti Latva‐aho'] Abstract—Wireless networks are inherently graph-structured, which can be utilized in graph representation learning to solve complex wireless network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such that…
Alejandro A. Edera, Georgina Stegmayer, Diego H. Milone
Unsupervised learning of node representations from knowledge graphs is critical for numerous downstream tasks, ranging from large-scale graph analysis to measuring semantic similarity between nodes. This study presents gGN as a novel representation that defines graph nodes as Gaussian distributions. Unlike existing…
Md Toki Tahmid, Tanjeem Azwad Zaman, Mohammad Saifur Rahman
Understanding complex graph-structured data is a cornerstone of modern research in fields like cheminformatics and bioinformatics, where molecules and biological systems are naturally represented as graphs. However, traditional graph neural networks (GNNs) often fall short by focusing mainly on node features while…
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…
Ragini Kihlman, Ilkka Launonen, Mikko J Sillanpää, Patrik Waldmann + 1 more
'D-J de Koning'] Title: Abstract In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated…
Wim Martens, Matthias Niewerth, Tina Popp, Stijn Vansummeren + 1 more
'Domagoj Vrgoč'] Modern graph database query languages such as GQL, SQL/PGQ, and their academic predecessor G-Core promote paths to first-class citizens in the sense that paths that match regular path queries can be returned to the user. This brings a number of challenges in terms of efficiency, caused by the fact that…
Tom C. Freeman, Sebastian Horsewell, Anirudh Patir, Josh Harling-Lee + 6 more
'Tim Regan' 'Barbara B. Shih' 'James Prendergast' 'David A. Hume' 'Tim Angus' 'Eli Zunder'] Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to…
Fawaz Dabbaghie
The representation of genomes and genomic sequences through graph structures has undergone a period of rapid development in recent years, particularly to accommodate the growing size of genome sequences that are being produced. Genome graphs have been employed extensively for a variety of purposes, including assembly…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Authors not listed
Genetic Algorithms are a powerful method to solve optimization problems with complex cost functions over vast search spaces that rely in particular on recombining parts of previous solutions. Crossover operators play a crucial role in this context. Here, we describe a large class of these operators designed for…
Maxim Kolomeets, Vasily Desnitsky, Igor Kotenko, Andrey Chechulin + 3 more
'Brij B. Gupta' 'Guangtao Zhai' 'Alicia García-Holgado'] Currently, the methods and means of human-machine interaction and visualization as its integral part are being increasingly developed. In various fields of scientific knowledge and technology, there is a need to find and select the most effective visualization…
Maria Boulougouri, Pierre Vandergheynst, Daniel Probst
Computational representation of molecules can take many forms, including graphs, stringencodings of graphs, binary vectors, or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regression tasks using a wide range of machine-learning models.…
Michael Hutcheon, Andrew Teale
Algorithms are presented for performing a topological analysis of an arbitrary function, evaluated on an arbitrary grid of points. These algorithms work strictly by post-processing the data and require no additional function evaluations. This is achieved by connecting the grid points with a neighbourhood graph…
Luca Cappelletti, Lauren Rekerle, Tommaso Fontana, Peter Hansen + 13 more
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…
Cristian Frăsinaru, Emanuel Florentin Olariu
Graph algorithms play an important role in many computer science areas. In order to solve problems that can be modeled using graphs, it is necessary to use a data structure that can represent those graphs in an efficient manner. On top of this, an infrastructure should be build that will assist in implementing common…
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…
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…
Authors not listed
We present a unified, set–theoretic framework that extends molecular graphs to hypergraphs and superhypergraphs via iterated power sets. We define Molecular Graphs, Molecular HyperGraphs, and Molecular SuperHyperGraphs, and develop four complements over them: Weighted, Rough, Neural, and Multipolar frameworks. We prove…
Liying Chen, Satwik Acharyya, Chunyu Luo, Yang Ni + 1 more
Probabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad…
Jianshu Zhao, Jean Pierre Both, Rob Knight
Graph/network representation learning (or graph/network embedding) is a widely used machine learning technique in industry recommending systems and has recently been applied in computational biology. Popular network representation learning algorithms include random walk and matrix factorization methods, but they do not…