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Full-Text Articles in Computer Sciences
Presto : Fast And Effective Group Closeness Maximization, Baibhav L. Rajbhandari
Presto : Fast And Effective Group Closeness Maximization, Baibhav L. Rajbhandari
Legacy Theses & Dissertations (2009 - 2024)
Given a graph and an integer k, the goal of group closeness maximization is to find, among all possible sets of k vertices (called seed sets), a set that has the highest group closeness centrality. Existing techniques for this NP-hard problem strive to quickly find a seed set with a high, but not necessarily the highest centrality.
Optimization Methods For Learning Graph-Structured Sparse Models, Baojian Zhou
Optimization Methods For Learning Graph-Structured Sparse Models, Baojian Zhou
Legacy Theses & Dissertations (2009 - 2024)
Learning graph-structured sparse models has recently received significant attention thanks to their broad applicability to many important real-world problems. However, such models, of more effective and stronger interpretability compared with their counterparts, are difficult to learn due to optimization challenges. This thesis presents optimization algorithms for learning graph-structured sparse models under three different problem settings. Firstly, under the batch learning setting, we develop methods that can be applied to different objective functions that enjoy linear convergence guarantees up to constant errors. They can effectively optimize the statistical score functions in the task of subgraph detection; Secondly, under stochastic learning setting, …
An Efficient System For Subgraph Discovery, Aparna Joshi
An Efficient System For Subgraph Discovery, Aparna Joshi
Legacy Theses & Dissertations (2009 - 2024)
Subgraph discovery in a single data graph---finding subsets of vertices and edges satisfying a user-specified criteria---is an essential and general graph analytics operation with a wide spectrum of applications. Depending on the criteria, subgraphs of interest may correspond to cliques of friends in social networks, interconnected entities in RDF data, or frequent patterns in protein interaction networks to name a few. Existing systems usually examine a large number of subgraphs while employing many computers and often produce an enormous result set of subgraphs. How can we enable fast discovery of only the most relevant subgraphs while minimizing the computational requirements?