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37 results for “graph analysis”
Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis - Supplementary Material
<p>Supplementary Material for paper: "Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis", submitted to BPMDS 2024</p>
Dataset for survey on applications of graph analysis
<p>This archive contains data collected and processed while conducting our survey on graph analysis applications. We provide data from three stages of our process to facilitate validation and reproduction of our results. All results are provided in TSV format (Tab-Separated Values). The completed survey will be submitted for publication in the near future.</p> <p>The file "1-query-results.tsv" contains the top 100 search results for 8 queries performed using Google Scholar. For each result we include the query used, the title of the result, and a link to the publication.</p> <p>The file "2-filtered-relevant-results.tsv" contains the subset of search results we found to be relevant, i.e., the work describes an application of graph analysis.</p> <p>The file "3-characterization-of-selected-results.tsv" contains a selection of 60 applications (articles) we reviewed and characterized based on our taxonomy for graph analysis applications. Included are the domain of each application and a set of characteristics. For each characteristic, a value of 1 represents that we consider the characteristic to be present in the surveyed application. Legend for characteristics:</p> <p> Graph features:<br> - Directed<br> - Weighted: vertex (V), edge (E), edge weights removed after filtering low weight edges from input (*).<br> - Heterogeneous: vertex (V), edge (E).<br> - Properties: vertex (V), edge (E).<br> - Temporal<br> Analysis methods:<br> - Neighborhood Statistics: clustering coefficient (C), degree distribution (D), other (*).<br> - Paths & Traversals: shortest path (S), average path length (A), maximum path length (M), traversal (T), other (*).<br> - Connectivity: weakly connected components (W), strongly connected components (S).<br> - Centrality & Ranking: betweenness centrality (B), closeness centrality (C), degree centrality (D), PageRank (P), other (*).<br> - Clustering: rule-based (R), community-based (C).<br> - Subgraph Isomorphism & Mining: pattern detection (P), subgraph isomorphism (S), frequent subgraph mining (F), discriminative subgraph mining (D).<br> - Graph Mutation: construction (C), reduction (R).<br> </p>
Call Graph Soundness in Android Static Analysis
<pre>## Artifact Folder Structure Below is a brief explanation of each directory of our artifacts: - **dataset/**: Contains the dataset used in our study. - **dynamic_analysis/**: Contains everything needed to reproduce our dynamic analysis experiments. - **static_analysis/**: Contains everything needed to reproduce our static analyses experiments. - **instrumentation/**: Contains the necessary files to instrument the apps for the dynamic analysis. - **SLR/**: Contains the excel files with the papers collected during our Systematic Literature Review (SLR). - **results/**: Contains the results of our static analysis. Please ensure that all the necessary files and resources are present in the respective directories before running any experiments.</pre>
Multi-omics data analysis for rare population inference using single-cell graph transformer
<p>## MarsGT: For rare cell identification from matched scRNA-seq (snRNA-seq) and scATAC-seq (snATAC-seq),includes genes, enhancers, and cells in a heterogeneous graph to simultaneously identify major cell clusters and rare cell clusters based on eRegulon.</p> <p>## Data Collection The data was collected using GEO Database.</p> <p>## Data Format The data is stored as TSV file and MTX file where each row represents a gene and each column represents a sample. </p> <p>## Variables - Gene IDs: Gene Symbols (e.g., MALAT1) - Sample IDs: Sample identifiers (e.g., AAACATGCAAATTCGT-1) - Expression level: Row gene expression level.</p>
Contributions From the Analysis of Graphs for Identification of Neural Cliques
ClinicalTrials.gov study NCT02305771. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Discovering biogeographic and ecological clusters with a graph theoretic spin on factor analysis
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Simulated read data analysed in "Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph"
<p>Simulated read data analyzed in "Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph".</p> <p><strong>1) Human sequence data</strong></p> <p><strong>HO_chr11_50bp_sliding_window*fq.gz:</strong><br> All possible 50 bp reads overlapping chromosome 11 SNPs in the Human Origins dataset. Files with the word "alternate" in their filename carry the alternate allele, otherwise, they carry the reference allele. Deamination has been added into these simulated reads using gargammel (Renaud 2016) based on empirically estimated post-mortem damage in a dataset of 102 ancient genomes (Allentoft et al., 2015).</p> <p><strong>2) microbial data</strong></p> <p><strong>simulation_*_s.fq.gz:</strong><br> Simulated microbial read data from a set of microbial reference genomes identified in the ancient Clovis genome (Rasmussen 2014), using gargammel.</p>
Data from: Empirical and theoretical study of Atelostomate (Echinoidea, Echinodermata) plate architecture: using graph analysis to reveal structural constraints
Describing patterns of connectivity among organs is essential for identifying anatomical homologies among taxa. It is also critical for revealing morphogenetic processes and the associated constraints that control the morphological diversification of clades. This is particularly relevant for studies of organisms with skeletons made of discrete elements such as arthropods, vertebrates, and echinoderms. Nonetheless, relatively few studies devoted to morphological disparity have considered connectivity patterns as a level of morphological organization or developed comparative frameworks with proper tools. Here, we analyze connectivity patterns among apical plates in Atelostomata, the most diversified clade among irregular echinoids. The clade comprises approximately 1600 fossil and Recent species (e.g., 25% of post-Paleozoic species of echinoids) and shows high levels of morphological disparity. Plate connectivity patterns were analyzed using tools and statistics of graph theory. To describe and explore the diversity of connectivity patterns among plates, we symbolized each pattern as a graph in which plates are coded as nodes that are connected pairwise by edges. We then generated a comparative framework as a morphospace of connections, in which the disparity of plate patterns observed in nature was mapped and analyzed. Main results show that apical plate patterns are both highly disparate between and within atelostomate groups and limited in number; overall, they also constitute small, compact, and simple structures compared to possible random patterns. Main traits of the evolution of apical plate patterns reveal the existence of strong morphogenetic constraints that are phylogenetically determined. In contrast, evolutionary radiations within atelostomates were accompanied by a clear increase in disparity, suggesting a release of some constraints at the origin of clades.
Cost Analysis Results of the Generation Circuits of the Entanglement Classes of Graph States with up to 7 Qubits
<p>This dataset encompasses a comprehensive analysis of entanglement classes of graph states. We have explored all locally equivalent graphs, along with every isomorphism, for each of the 45 entanglement classes associated with graph states featuring 7 qubits or fewer. The results, detailing various parameters of the quantum circuits generated for all distinct isomorphisms within each graph and local equivalence class, are stored in CSV files. </p>
Generated random graphs for significance analysis
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Functionally Important Residues from Graph Analysis of Co-evolved Dynamical Couplings
<p>This dataset contains input files and trajectories for class A β-lactamase SHV-1.</p>
Data from: Graphs in phylogenetic comparative analysis: Anscombe’s quartet revisited
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Data from: Empirical and theoretical study of Atelostomate (Echinoidea, Echinodermata) plate architecture: using graph analysis to reveal structural constraints
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Data from: Graph analysis of cell clusters forming vascular networks
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Impaired brain network architecture in Cushing’s disease based on graph theoretical analysis
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Analysis Titanic Survival through Graph-Based Node with Neo4j: Unraveling Social Dynamics (Dataset & Cypher)
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Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.