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 99 for “"Community detection"”.
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COMMUNITY DETECTION IN GRAPHS
Community detection has always 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 …
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Community Detection in Complex Networks
… block model is a powerful tool for inferring community structure from network topology. However, the simple block model considers community structure as the only underlying attribute for forming the relational interactions among the nodes, this makes it prefer a Poisson degree distribution …
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Community Detection in Complex Networks
… an ensemble learning scheme and a new metric for community detection in complex networks. The scheme uses a Machine Learning algorithmic paradigm we call Extremal EnsembleLearning. It uses iterative extremal updating of an ensemble of network partitions, which can be found by a conventional base …
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Scaling overlapping community detection algorithms
Community structure is observed in many real-world networks in fields ranging from social networking to biological networks. Over the last decade many approaches have been proposed to efficiently detect the underlying structure of communities in graphs with a greater degree of correctness. This …
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Leaders, followers, and community detection
… maximal clique of the social network graph as a community. The problem of finding maximal cliques is known to be computationally hard. The goal of this work is to identify structural conditions in social network graphs that lead to efficient identification of maximal cliques, i.e. overlapping …
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Community Detection in Multi-Layer Networks
… colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and …
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Community detection in preferential attachment graphs
This thesis examines the problem of community detection in a new random graph model, which is a generalization of preferential attachment graphs. This model has some features that are more realistic than those of the often-studied stochastic block model (SBM). A message passing algorithm for …
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A comparison of community search with community detection
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Measuring Backtracking on Delivery Routes through Community Detection
… route settings. Our measurement method utilizes community detection, a group of machine learning algorithms for clustering nodes within graphs, based on edge structure and weight. We then investigate the ability of backtracking, as measured by our community detection-based method, to predict …
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Spectral analysis in bipartite biregular graphs and community detection
This thesis concerns to spectral gap of random regular graphs and consists of two main con- tributions. First, we prove that almost all bipartite biregular graphs are almost Ramanujan by providing a tight upper bound for the non trivial eigenvalues of its adjacency operator, proving Alon's …
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Advancing Community Detection through Ensemble Learning and Modularity Maximization
… to the domain of bipartite networks, where community detection presents the unique challenge of the resolution limit in modularity-based methods. We first demonstrate how a benchmark bipartite network fails to resolve smaller communities using traditional modularity. We then introduce a new …
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Lattice models of glasses and Potts models for community detection
… In Part II, we change our focus and examine community detection in graphs from a theoretical standpoint. Many disparate community definitions have been proposed, however except for one, few have been analyzed in any great detail. In this work, we, for the first time, formally study a …
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Improving the accuracy of community detection methods using connectivity modifier
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01
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Consistent community detection in uni-layer and multi-layer networks
… in literature. As with uni-layer networks, community detection is an important task in multi-layer networks. This dissertation aims to develop new methods and theory for community detection in both uni-layer and multi-layer networks that can be used to answer scientific questions from …
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Efficient Community Detection for Large Scale Networks via Sub-sampling
… Some of the networks have an inherent community structure based on interactions. The problem of identifying this grouping structure given a graph is termed as community detection problem which has certain existing algorithms. This thesis contributes by providing specific improvements to …
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Computational intelligence for community detection in complex networks and bio-medical applications
Contains fulltext : 129790.pdf (Publisher’s version ) (Open Access)
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On Graph Clustering or Community Detection: A Characteristic Analysis and Its Implications
… 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 levels of depth and in broad applications. The main objectives of graph clustering are to capture, characterize …
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Cluster damage robustness analysis and space independent community detection in complex networks
… The second novelty is the first application of a community detection method, which uncovers space-independent communities in spatial networks, to airport and linguistic networks. A critical property of complex systems – robustness – is explored within a partial model of the Internet, by …
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