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.…
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Kyoham Shin + 1 more
'Seokho Kang'] Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases with the number of nodes in a graph, has been a major challenge. This has hindered the application of deep learning-based molecular graph generation…
Muhammad Ifte Islam, Farhan Tanvir, Ginger Johnson, Esra Akbas + 1 more
Network embedding that encodes structural information of graphs into a low-dimensional vector space has been proven to be essential for network analysis applications, including node classification and community detection. Although recent methods show promising performance for various applications, graph embedding still…
Morihiro Hayashida, Tatsuya Akutsu
Background Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks…
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…
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.…
Ferhat Ay, Michael Dang, Tamer Kahveci
Metabolic network alignment is a system scale comparative analysis that discovers important similarities and differences across different metabolisms and organisms. Although the problem of aligning metabolic networks has been considered in the past, the computational complexity of the existing solutions has so far…
Sebastian E. Ahnert
We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the…
Timo Beller, Enno Ohlebusch
Background Recently, Marcus et al. (Bioinformatics 30:3476-83, [5]) proposed to use a compressed de Bruijn graph to describe the relationship between the genomes of many individuals/strains of the same or closely related species. They devised an $On\log g$ time algorithm called splitMEM that constructs this graph…
Amirmohammad Farzaneh, Justin P. Coon, Mihai-Alin Badiu, Narsis A. Kiani + 2 more
'Narsis A. Kiani' 'Hector Zenil' 'Jesper Tegnér'] Throughout the years, measuring the complexity of networks and graphs has been of great interest to scientists. The Kolmogorov complexity is known as one of the most important tools to measure the complexity of an object. We formalized a method to calculate an upper…
Wenfei Fan, Yuanhao Li, Muyang Liu, Can Lu
This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete parts and regular structures into supernodes. The supernodes carry a synopsis $S_\mathcal{Q}$ for each query class $\mathcal{Q}$ in use, to abstract key features of the contracted parts for answering queries of $\mathcal{Q}$.…
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…
Nguyen Gia Bach, Chanh Minh Tran, Tho Nguyen Duc, Phan Xuan Tan + 2 more
'Eiji Kamioka' 'Yitzhak Yitzhaky'] In light field compression, graph-based coding is powerful to exploit signal redundancy along irregular shapes and obtains good energy compaction. However, apart from high time complexity to process high dimensional graphs, their graph construction method is highly sensitive to the…
Phanindra Reddy Madduru, Bijo Thomas
This paper proposes a preprocessing framework for optimizing large-scale graph database ingestion through intelligent edge filtering based on value ranking. We combine adapted PageRank algorithms with business-specific metrics and edge type importance to evaluate and rank edges, enabling selective retention of…
David Podgorelec, Damjan Strnad, Ivana Kolingerová, Borut Žalik + 1 more
'Jun Chen'] After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others…