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Showing 1 to 20 of 28 for “"graph clustering"”.
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Fast and Accurate Graph Clustering
Graph clustering, also known as community detection, is a fundamental problem in graph analytics with applications across a wide variety of domains including bioinformatics, social media analysis, and anomaly detection. Graph clustering algorithms can be grouped into two categories: inferential and …
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Parameterfreies hierarchisches Graph-Clustering-Verfahren zur Interpretation raumbezogener Daten
… in raumbezogenen Daten innerhalb von Geographischen Informationssystemen (GIS) voraus. Zunächst beschreiben wir einen Ansatz zur Generierung von 3D-Gebäuden, welche als Hypothese aus Katasterkarten abgleitet werden. Diese Vorgehensweise stellt ein Beispiel für die DLM-Interpretation auf …
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Deep Graph Representation Learning and its Application on Graph Clustering
Graphs like social networks, molecular graphs, and traffic networks are everywhere in the real world. Deep Graph Representation Learning (DGL) is essential for most graph applications, such as Graph Classification, Link Prediction, and Community Detection. DGL has made significant progress in …
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Parallel Algorithms, Optimizations, and Benchmarks for Metric and Graph Clustering
Clustering is a fundamental unsupervised machine learning task of detecting groups of similar objects in data. Clustering can be used to identify the underlying substructures of data and can detect essential functional groups, such as people with similar interests, news articles on similar topics, …
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On Graph Clustering or Community Detection: A Characteristic Analysis and Its Implications
<p>Graphs or networks represent relational data in an abstract and unifying form in terms of vertices and edges. Graph clustering in graph study language, or community detection in network science and engineering language, is fundamental to exploratory analysis of relational data, at different …
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Using Graph Clustering to Analyze the Spread of an Infectious Disease on a Random Large Social Network Graph
… disease on a random large social network graph. The goal is to determine if graph clustering techniques are a viable option to reduce workload of analyzing of a large data set. A random graph generator was developed using characteristics from the Forest Fire Model. We then use this graph …
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Bridging Theory and Practice in Parallel Clustering
Large-scale graph processing is a fundamental tool in modern data mining, with wide-ranging applications in domains including social network analysis, bioinformatics, and machine learning. In particular, graph clustering, or community detection, is an important problem in graph processing that …
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Biological Networks: Modeling and Structural Analysis
… For protein complex networks, we propose a hypergraph model which more accurately represents the data than earlier models. We define the concept of <em>k</em>-cores in hypergraphs, which are highly connected subhypergraphs, and design an algorithm for computing <em>k </em>-cores in hypergraphs. A …
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Distributed Computing for Large-scale Graphs
… Since many of these sources can be modeled as graphs, many large-scale graph processing frameworks have been developed, from vertex-centric models such as pregel to more complex programming models that allow asynchronous computation, can tackle dynamism in the data and permit the usage of …
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Investigating the fine grained structure of networks
… a novel representation for characterizing a graph's fine grained structure. The key idea is that this structure can be represented as a distribution of the structural features of subgraphs. I introduce a set of such structural features and use them to compute representations for a variety of …
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Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning
… where we coarse-grain a physical system using graph clustering, and model the system evolution with a very large time-integration step using graph neural networks. Despite only trained with short MD trajectory data, our learned simulator can generalize to unseen novel systems and simulate for …
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Distributed graph decomposition algorithms on Apache Spark
… analysis and mining of large and complex graphs for describing the characteristics of a vertex or an edge in the graph have widespread use in graph clustering, classification, and modeling. There are various methods for structural analysis of graphs including the discovery of frequent …
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Scalable algorithms for correlation clustering on large graphs
Correlation clustering (CC) is a widely-used clustering paradigm, where objects are represented as graph nodes and clustering is performed based on relationships between objects (positive or negative edges between pairs of nodes). The CC objective is to obtain a graph clustering that minimizes the …
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Data Association Algorithms and Representations for Robust Geometric Perception
… pairwise association problem using a weighted graph, large complete subgraphs of highly consistent associations can be found without sacrificing information through thresholding, unlike previous methods. The second contribution is the introduction of a novel representation for lines and planes …
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Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering
Large graph-based datasets are common to many applications because of the additional structure provided to data by graphs. Patterns extracted from graphs must adhere to these structural properties, making them a more complex class of patterns to identify. The role of graph mining is to efficiently …
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Interpretable Network Representations
<p>Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation …
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Algorithms and Theory for Robust PCA and Phase Retrieval
… by PCP. Our results yield new insights on the graph clustering problem beyond the relevant literature. </p> <p>The second part of the thesis studies the phase retrieval problem, which requires recovering an unknown vector from only its magnitude measurements. Differently from the first part, we …
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The integration of fine-scale DNA-DNA associations by inclusion of Hi-C DNA cross-linking information into metagenomic community analysis
… 𝘪𝘯 𝘴𝘪𝘭𝘪𝘤𝘰 investigation of the effectiveness of graph clustering as a means of metagenome deconvolution was conducted; where Hi-C proximity interactions defined the edges and assembly contigs defined the nodes. A parametric sweep of experimental and community composition parameters was carried …
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Network Based Approaches for Clustering and Location Decisions
… is to study commonly occurring location and clustering problems on graphs. The dissertation is presented as a collection of results in topics including finding maximum cliques in large graphs, graph clustering in large scale graphs, determining location of facilities for pre-positioning …
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Power network and smart grids analysis from a graph theoretic perspective
… recently, a few fundamental concepts from graph theory have also been applied, for example in symmetry-based cluster synchronization. Among the existing notions of graph theory, graph symmetry is the focus of this proposal. However, there are other development around some concepts from …
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