graph partitioning and sparse reconstruction UNIVERSITY OF FLORIDA William Hager PhanDung TienIn this dissertation, we develop theories and practical algorithms for optimization problems which arise from science, economics and engineering.Phan, Dung TienDissertations & Theses - Gradworks
(宸茶 )Using graph partitioning for efficient network modularity optimizationDjidjev, HristoOnus, MelihHristo Djidev,Melih Onus.Using graph partitioning for efficient network modularity optimization. Contemporary Mathematics . 2013
On the other hand, it is shown to be NP-complete to get a optimal partitioning result [12], which means that frequently partitioning the graph from the scratch is computationally unaffordable. These present great computational challenges to our problem. The state-of-the-art incremental method In...
The TigerGraph Native Parallel Graph offers a transformational technology, with significant clear advantages over the most well-known graph database solutions on the market. Despite its comprehensive and well-documented graph database functionality, the current leading solution is considerably slower in com...
Chevalier, C., Safro, I.: Comparison of coarsening schemes for multi-level graph partitioning. In: Proceedings Learning and Intelligent Optimization (2009) Google Scholar Chierichetti, F., Kumar, R., Lattanzi, S., Mitzenmacher, M., Panconesi, A., Raghavan, P.: On compressing social netw...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing ...
A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects 4.2.1 Hard/Crisp CLustering Each data object belongs to only one cluster in a hard or crisp clustering algorithm. The clustering methods under this categ...
To do so, we must address challenges introduced by real- life large graphs: • The irregular structure of real graphs leads to poor paral- lelism. In general, the logic of a partitioning algorithm is complex and many computation steps may depend heavily on each other, leading to poor ...
[ 35 ] On Consistency in Graph Neural Network Interpretation 标题:关于图神经网络解释中的一致性问题 链接:https://arxiv.org/abs/2205.13733 [ 36 ] Faster Optimization on Sparse Graphs via Neural Reparametrization 标题:基于神经重构的稀疏图快速优化 ...
To overcome these challenges, we proposed LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generated frequent query patterns according to the user’s historical query subgraphs and then performed labeled graph partitioning based on the frequent...