26 papers · ranked by Valyu relevance
Haochen Chen, Bryan Perozzi, Rami Al‐Rfou, Steven Skiena
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by…
Anthony Baptista, Rubén J. Sánchez-García, Anaı̈s Baudot, Ginestra Bianconi
'Ginestra Bianconi'] 1School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, United Kingdom; 2The Alan Turing Institute, The British Library, London, NW1 2DB, United Kingdom; 3School of Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom; 4 Institute for…
Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu
Network embedding assigns nodes in a network to lowdimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development…
Weiwei Gu, Aditya Tandon, Yong-Yeol Ahn, Filippo Radicchi
Network embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension - small enough to be efficient and large enough to be effective - is challenging but necessary to generate embeddings applicable to a…
Sabrina Benbatata, Bilal Saoud, Ibraheem Shayea, Naif Alsharabi + 5 more
'Abdulraqeb Alhammadi' 'Ali Alferaidi' 'Amr Jadi' 'Yousef Ibrahim Daradkeh' 'José Alberto Benítez-Andrades'] In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the…
Wei Wu, Guangmin Hu, Fucai Yu, Kevin H. Knuth
In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural networks (GNN). GCN, namely, graph convolution networks, are neural…
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…
Amina Amara, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha
Real-world information networks are increasingly occurring across various disciplines including online social networks and citation networks. These network data are generally characterized by sparseness, nonlinearity and heterogeneity bringing different challenges to the network analytics task to capture inherent…
Zhenhua Huang, Zhenyu Wang, Rui Zhang, Yangyang Zhao + 2 more
'Sharad Mehrotra'] Abstract—Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding…
Léo Pio-Lopez, Alberto Valdeolivas, Laurent Tichit, Élisabeth Remy + 1 more
'Anaïs Baudot'] Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically…
Poorya Parvizi, Francisco Azuaje, Evropi Theodoratou, Saturnino Luz
A network embedding approach reduces the analysis complexity of large biological networks by converting them to lowdimensional vector representations (features/embeddings). These lower-dimensional vectors can then be used in machine learning prediction tasks with a wide range of applications in computational biology…
Yi-Jiao Zhang, Kai‐Cheng Yang, Filippo Radicchi
Network embedding techniques aim at representing structural properties of graphs in geometric space. Those representations are considered useful in downstream tasks such as link prediction and clustering. However, the number of graph embedding methods available on the market is large, and practitioners face the…
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
Mustafa Coşkun, Mehmet Koyutürk
Link prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if…
Paola Lecca, Michela Lecca
Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually…
Edwin Alvarez-Mamani, Reinhard Dechant, César A. Beltran-Castañón, Alfredo J. Ibáñez
'Alfredo J. Ibáñez'] Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph…
Farzaneh Heidari, Manos Papagelis
Large-scale network mining and analysis is key to revealing the underlying dynamics of networks, not easily observable before. Lately, there is a fast-growing interest in learning low-dimensional continuous representations of networks that can be utilized to perform highly accurate and scalable graph mining tasks. A…
Yue Hu, Svitlana Oleshko, Samuele Firmani, Zhaocheng Zhu + 7 more
Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine. Traditional link prediction (LP) methods, using similarity metrics like Personalized PageRank, are limited in capturing the complexity of biological networks. Recently, representation-based learning techniques have…
Renming Liu, Matthew Hirn, Arjun Krishnan
Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction…
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…
Haotong Sun, Yinghui Jiang, Minhao Wang, Xianglu Xiao + 5 more
Rapid and accurate prediction of molecular properties is a fundamental task in drug discovery. In recent years, deep learning-based molecular property prediction methods have received much attention and recent successes have shown that learning the representations of molecular structures by applying graph neural…
Benjamin Ries, Richard J Gowers, James RB Eastwood, Irfan Alibay + 4 more
Alchemical free energy campaigns can be planned using graph theory by building up networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Jenke Scheen, Mark Mackey, Julien Michel
Relative binding free energy (RBFE) calculations are increasingly used to support the ligand optimisation problem in early-stage drug discovery. Because RBFE calculations frequently rely on alchemical perturbations between ligands in a congeneric series, practitioners are required to estimate an optimal combination of…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
Philipp-Maximilian Jacob, Alexei Lapkin
Is chemistry discoverable or can it only be invented? – this is the question of a computer scientist and a philosopher of science when looking at application of artificial intelligence methods for developing new chemical entities and new chemical transformations. This study confirms that, at least today, chemistry is…