Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 35 for “"graph mining"”.
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Fair and robust graph mining
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Algorithmic foundation of fair graph mining
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Multi-facet graph mining with contextualized projections
… research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various …
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Accurate and Fast Approximate Graph Mining at Scale
Approximate graph pattern mining (A-GPM) is an important data analysis tool for numerous graph-based applications. There exist sampling-based A-GPM systems to provide automation and generalization over a wide variety of use cases. Despite improved usability, there are two major obstacles that …
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Predictive analysis of real-time strategy games using graph mining
… model relationships between entities is by using graphs. The vast amount of data has resulting in complex and large graphs that are difficult to process. Hence, researchers frequently employ parallelized or distributed processing. But first, the graph data must be partitioned and assigned to …
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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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Sweep-Line Extensions to the Multiple Object Intersection Problem: Methods and Applications in Graph Mining
… can effectively inform a number of spatial data mining methods and can provide support in decision making for a variety of critical applications. The state-of-the-art approach for addressing such problems resorts to an algorithmic paradigm, collectively known as the sweep-line or plane sweep …
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Scaling overlapping community detection algorithms
… the underlying structure of communities in graphs with a greater degree of correctness. This specific area of graph mining is known as community detection. It is one of the most critical components of graph mining. It helps in understanding the underlying properties of the graph. While a lot …
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Declarative Analytics on Heterogeneous HPC Systems
… scalable declarative analytics across big data, graph mining, and program analysis on HPC systems. While recent advancements have focused on multi-threaded and multi-core implementations of Datalog, the evolution of exascale systems presents a compelling opportunity to extend Datalog’s …
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Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery
Network analysis and graph mining play a prominent role in providing insights and studying phenomena across various domains, including social, behavioral, biological, transportation, communication, and financial domains. Across all these domains, networks arise as a natural and rich representation …
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Probabilistic Models and Algorithmic Analysis of Network Problems
… computing, and the other is in the domain of graph mining. We approach these problems by probabilistic tools, to model, analyze, and design algorithms. In the first problem, we aim to improve upon the known bounds of some fundamental distributed algorithms, Minimum Spanning Tree (MST) in …
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Evaluating and Forecasting the Operational Performance of Road Intersections
… operational performance of road intersections by mining streams of V2I data. Our system makes use of graph mining and trajectory data mining methods to continuously evaluate a set of well-defined measures of effectiveness (MOEs) for traffic operations at different levels of road network …
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Large-Scale Constraint-Based Pattern Mining
… studied the problem of constraint-based pattern mining for three different data formats, item-set, sequence and graph, and focused on mining patterns of large sizes. Colossal patterns in each data formats are studied to discover pruning properties that are useful for direct mining of these …
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Identifying Splicing Regulatory Elements with de Bruijn Graphs
… identifying variable length SREs utilizing a graph-based model with de Bruijn graphs and discovering co-occurring sets of SREs (combinatorial SREs) utilizing graph mining techniques. In addition, I studied and analyzed the effect of alternative splicing on tissue specificity in human. First, I …
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Uncovering latent structure in social networks using graph embeddings
… has been one of the commonly studied problems of graph mining, and is recognized as a challenging necessary task, and many open tasks are still poorly understood. We show that user information from social network platforms such as Instagram can be clustered using similarities based independently …
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Motif Mining On Structured And Semi-structured Biological Data
… in biology such that the data is structured (graphs)and semi-structured (sequences).A challenge of motif mining in sequences is the existence of variationsincluding substitutions and permutations. Taking into account the existenceof these two kinds of variations, we propose a novel sequential …
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Evolving Network Representation Learning Based on Random Walks
Large-scale network mining and analysis is key to revealing the underlying dynamics of networks, not easily observable before. Lately, there is a fast-growing interest in learning low-dimensional continuous representations of networks that can be utilized to perform highly accurate and scalable …
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Scalable and Tunable Algorithms for Adaptive All-to-all Data Exchange
… These include data-driven machine learning, graph mining, fast Fourier transform, quantum computer simulations, and certain advanced preconditioners and solvers. Implementing all-to-all in a manner that scales for different workload sizes, distribution patterns, process counts, and hardware …
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Optimisation techniques for finding connected components in large graphs using GraphX
… of finding connected components in undirected graphs has been well studied. It is an essential pre-processing step to many graph computations, and a fundamental task in graph analytics applications, such as social network analysis, web graph mining and image processing. Recently, it has been a …
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COMMUNITY DETECTION IN GRAPHS
… been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same community while separating …
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