15 papers · ranked by Valyu relevance
Muhammad Umair, Young-Koo Lee
Graph data are pervasive worldwide, e.g., social networks, citation networks, and web graphs. A real-world graph can be huge and requires heavy computational and storage resources for processing. Various graph compression techniques have been presented to accelerate the processing time and utilize memory efficiently.…
Prathyush Poduval, Haleh Alimohamadi, Ali Zakeri, Farhad Imani + 3 more
'M. Hassan Najafi' 'Tony Givargis' 'Mohsen Imani'] Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their…
Luca Cappelletti, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr + 7 more
'Tiffany J. Callahan' 'Carlos Cano' 'Marcin P. Joachimiak' 'Christopher J. Mungall' 'Peter N. Robinson' 'Justin Reese' 'Giorgio Valentini'] Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise…
Alexis Bénichou, Jean-Baptiste Masson, Christian L. Vestergaard, Fabrizio De Vico Fallani
Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement…
Tangina Sultana, Young-Koo Lee, Wookey Lee
The explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. Although extensive work has been carried out for compressing the RDF datasets, they do not perform well in all dimensions.…
Wenxing Hu, Xianke Zhan, Minglei Tong, Zahir M. Hussain
A massive number of paper documents that include important information such as circuit schematics can be converted into digital documents by optical sensors like scanners or digital cameras. However, extracting the netlists of analog circuits from digital documents is an exceptionally challenging task. This process…
Bo Peng, Huan Xu, Xiangjiu Che
Introduction Graph data representation is widely applicable in numerous real-world scenarios, and recent advances in graph neural networks (GNNs) have enabled effective modeling of complex associations in graph-structured data. However, GNNs are often constrained by the over-smoothing problem, which reduces their…
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…
Jie Han, Tao Guo, Qiaoqiao Zhou, Wei Han + 5 more
'Narsis A. Kiani' 'Hector Zenil' 'Jesper Tegnér'] With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while…
Xueyuan Chen, Shangzhe Li, Yanchun Liang
Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within contrastive learning, the selection of a “view” dictates the…
Guojian Deng, Changsheng Shi, Ruiquan Ge, Riqian Hu + 5 more
'Feiwei Qin' 'Cheng Pan' 'Haixia Mao' 'Qing Yang'] Background Predicting drug-target interaction (DTI) is a crucial phase in drug discovery. The core of DTI prediction lies in appropriate representations learning of drug and target. Previous studies have confirmed the effectiveness of graph neural networks (GNNs) in…
Chengxin Xie, Jingui Huang, Yongjiang Shi, Hui Pang + 3 more
'Xiumei Wen' 'Marco Piangerelli'] Graph auto-encoders are a crucial research area within graph neural networks, commonly employed for generating graph embeddings while minimizing errors in unsupervised learning. Traditional graph auto-encoders focus on reconstructing minimal graph data loss to encode neighborhood…
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…
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…
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…