27 papers · ranked by Valyu relevance
Fragkiskos D. Malliaros, Michalis Vazirgiannis
Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed – in the sense that there is directionality on the edges, making the semantics of the edges non symmetric as the source node…
R. Greg Stacey, Michael A. Skinnider, Leonard J. Foster
Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such…
Scott Emmons, Stephen Kobourov, Mike Gallant, Katy Börner + 1 more
Clustering is the task of assigning a set of objects to groups (also called classes or categories) so that the objects in the same cluster are more similar (according to a predefined property) to each other than to those in other clusters. This is a fundamental problem in many fields, including statistics, data…
Jungrim Kim, Mincheol Shin, Jeongwoo Kim, Chihyun Park + 7 more
As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched.…
Qi Li, Cody Nichols, Robert S Welner, Jake Y. Chen + 2 more
The integrative analysis of gene sets, networks, and pathways is pivotal for deciphering omics data in translational biomedical research. To significantly increase gene coverage and enhance the utility of pathways, annotated gene lists, and gene signatures from diverse sources, we introduced pathways, annotated gene…
Chao Tong, Jianwei Niu, Bin Dai, Zhongyu Xie
In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property. Network clustering methods are thus significant for the study of complex networks. Currently, many typical clustering algorithms have some weakness like inaccuracy and slow…
Evangelos Karatzas, Maria Gkonta, Joana Hotova, Fotis A. Baltoumas + 4 more
Clustering is the process of grouping together different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate…
Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods that, based on the dissimilarity structure, output hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter. Our construction of hierarchical…
Biswajit Saha, Amitabha Mandal, Soumendu Bikas Tripathy, Debaprasad Mukherjee
'Debaprasad Mukherjee'] This paper is an extensive survey of literature on complex network communities and clustering. Complex networks describe a widespread variety of systems in nature and society especially systems composed by a large number of highly interconnected dynamical entities. Complex networks like real…
Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
—This paper characterizes hierarchical clustering methods that abide by two previously introduced axioms – thus, denominated admissible methods – and proposes tractable algorithms for their implementation. We leverage the fact that, for asymmetric networks, every admissible method must be contained between reciprocal…
Sumit Kumar Singh
Graph clustering is a fundamental problem that has been extensively studied both in theory and practice. The problem has been defined in several ways in the literature and most of them have been proven to be NP-Hard. Due to their high practical relevancy, several heuristics for graph clustering have been introduced…
Sergio Antonio Alcalá-Corona, Santiago Sandoval-Motta, Jesús Espinal-Enríquez, Enrique Hernández-Lemus
'Jesús Espinal-Enríquez' 'Enrique Hernández-Lemus'] Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of…
Kazuki Nakajima, Kazuyuki Shudo
The measurement error of the network topology caused by missing network data during the collection process is a major concern in analyzing collected network data. It is essential to clarify the error between the properties of an original network and the collected network to provide an accurate analysis of the entire…
Orhan Çamoğlu, Tolga Can, Ambuj K. Singh
A protein network shows physical interactions as well as functional associations. An important usage of such networks is to discover unknown members of partially known complexes and pathways. A number of methods exist for such analyses, and they can be divided into two main categories based on their treatment of highly…
Mehrsa Pourya, Shayan Aziznejad, Michael Unser, Daniel Sage
We propose a novel method for the clustering of point-cloud data that originate from single-molecule localization microscopy (SMLM). Our scheme has the ability to infer a hierarchical structure from the data. It takes a particular relevance when quantitatively analyzing the biological particles of interest at different…
María Pereda, Ernesto Estrada
A complex network is a condensed representation of the relational topological framework of a complex system. A main reason for the existence of such networks is the transmission of items through the entities of these complex systems. Here, we consider a communicability function that accounts for the routes through…
Pierre Miasnikof, Alexander Y. Shestopaloff, Leonidas Pitsoulis, Yuri Lawryshyn
'Yuri Lawryshyn'] With a view on graph clustering, we present a definition of vertexto-vertex distance which is based on shared connectivity. We argue that vertices sharing more connections are closer to each other than vertices sharing fewer connections. Our thesis is centered on the widely accepted notion that strong…
Thomas E. Gorochowski, Claire S. Grierson, Mario di Bernardo
Network motifs are significantly expressed sub-graphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue we…
László A Zahoránszky, Gyula Y Katona, Péter Hári, András Málnási-Csizmadia + 2 more
'András Málnási-Csizmadia' 'Katharina A Zweig' 'Gergely Zahoránszky-Köhalmi'] Background Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the…
Mary Pitman, David Hahn, Gary Tresadern, David Mobley
Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the…
Kevin Mildau, Christoph Büschl, Jürgen Zanghellini, Justin J.J. van der Hooft
Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets…
Lionel Zoubritzky, François-Xavier Coudert
We present here an open-source Julia library for the topological identification of crystalline materials, with algorithmic and computational improvements over the previously available software in the field, resulting in a speed increase of one order of magnitude. This new algorithm and implementation can therefore be…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
Authors not listed
Because topology plays a key role in many chemical and physical properties of materials, identification of topology from crystalline structures is a common and important task in materials science. We present here a new web application, CrystalNets, whose user-friendly interface allows scientists to identify and…
Rongqing Tang, Jianhui Zhou, Nan Zhang
In recent years, polyoxometalate-based materials have attracted wide attention in heterogeneous photocatalysis due to their various structure types and controllable redox properties. In this review, 1382 scientific publications related to polyoxometalate-based photocatalysts in the Web of Science database were…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…