Search Results - "Graph Theory—graph algorithms"

The Logic of Graph Neural Networks by Grohe, Martin

“…Graph neural networks (GNNs) are deep learning architectures for machine learning problems on graphs. It has recently been shown that the expressiveness of…”
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Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications by Rush, Tomás A., Shrestha, Him K., Gopalakrishnan Meena, Muralikrishnan, Spangler, Margaret K., Ellis, J. Christopher, Labbé, Jesse L., Abraham, Paul E.

“…Natural products derived from microbes are crucial innovations that would help in reaching sustainability development goals worldwide while achieving…”
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The neuroanatomical organization of the hypothalamus is driven by spatial and topological efficiency by Smith, Nathan R., Ameen, Shabeeb, Miller, Sierra N., Kasper, James M., Schwarz, Jennifer M., Hommel, Jonathan D., Borzou, Ahmad

“…The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly…”
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The Readability of Path-Preserving Clusterings of Graphs by Archambault, Daniel, Purchase, Helen C., Pinaud, Bruno

“…Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in…”
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Lovász-Type Theorems and Game Comonads by Dawar, Anuj, Jakl, Tomas, Reggio, Luca

“…Lovász (1967) showed that two finite relational structures A and B are isomorphic if, and only if, the number of homomorphisms from C to A is the same as the…”
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Eigenvector Centrality Dynamics From Resting-State fMRI: Gender and Age Differences in Healthy Subjects by Wink, Alle Meije

“…With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become…”
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Hydrogen-bond networks for proton couplings in G-Protein coupled receptors by Bondar, Ana-Nicoleta, Alfonso-Prieto, Mercedes

“…G-protein signaling pathways mediate communication across cell membranes. The first steps of this communication occur at the cell membrane, where upon…”
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Local Higher-Order Graph Clustering by Yin, Hao, Benson, Austin R, Leskovec, Jure, Gleich, David F

“…Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable…”
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Graph Representation Learning for Microarchitecture Design Space Exploration by Yi, Xiaoling, Lu, Jialin, Xiong, Xiankui, Xu, Dong, Shang, Li, Yang, Fan

“…Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic…”
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A Visualization Framework and User Studies for Overloaded Orthogonal Drawings by Didimo, Walter, Kornaropoulos, Evgenios M., Montecchiani, Fabrizio, Tollis, Ioannis G.

“…Overloaded orthogonal drawing (OOD) is a recent graph visualization style specifically conceived for directed graphs. It merges the advantages of some popular…”
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Clone detection in automotive model-based development by Deissenboeck, F., Hummel, B., Jurgens, E., Schatz, B., Wagner, S., Girard, J.-F., Teuchert, S.

“…Model-based development is becoming an increasingly common development methodology. In important domains like embedded systems already major parts of the code…”
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GraphFI: An Efficient Fault Injection Framework for Graph Processing on GPGPUs by Jiang, Nan, Yue, Hengshan, Tan, Jingweijia, Zhou, Mengting, Wang, Xiaonan, Wang, Yuchun, Wei, Wenda, Qiu, Meikang, Wei, Xiaohui

“…As graph tasks become pervasive in real-time and safetycritical domains (e.g., financial fraud detection and electrical power systems), it is also essential to…”
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PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration by Yang, Tao, Li, Dongyue, Han, Yibo, Zhao, Yilong, Liu, Fangxin, Liang, Xiaoyao, He, Zhezhi, Jiang, Li

“…Graph Convolutional Network (GCN) is a promising but computing- and memory-intensive learning model. Processing-in-memory (PIM) architecture based on the ReRAM…”
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GraphCoder: Enhancing Repository-Level Code Completion via Coarse-to-fine Retrieval Based on Code Context Graph by Liu, Wei, Yu, Ailun, Zan, Daoguang, Shen, Bo, Zhang, Wei, Zhao, Haiyan, Jin, Zhi, Wang, Qianxiang

“…The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the…”
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TurboBC: A Memory Efficient and Scalable GPU Based Betweenness Centrality Algorithm in the Language of Linear Algebra by Artiles, Oswaldo, Saeed, Fahad

“…Betweenness centrality (BC) is a shortest path centrality metric used to measure the influence of individual vertices or edges on huge graphs that are used for…”
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iG-kway: Incremental k-way Graph Partitioning on GPU by Lee, Wan Luan, Jiang, Shui, Lin, Dian-Lun, Chang, Che, Zhang, Boyang, Chung, Yi-Hua, Schlichtmann, Ulf, Ho, Tsung-Yi, Huang, Tsung-Wei

“…Recent advances in GPU-accelerated graph partitioning have achieved significant performance gains but remain limited to full graph partitioning, lacking…”
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A Scalable and Robust Compilation Framework for Emitter-Photonic Graph State by Ren, Xiangyu, Huang, Yuexun, Liang, Zhiding, Barbalace, Antonio

“…Quantum graph states are critical resources for various quantum algorithms, and also determine essential interconnections in distributed quantum computing…”
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HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering by Aghdaei, Ali, Feng, Zhuo

“…This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge…”
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BlasPart: A Deterministic Parallel Partitioner for Balanced Large-Scale Hypergraph Partitioning by Tong, Shengbo, Pei, Chunyan, Yu, Wenjian

“…Balanced hypergraph partitioning is a fundamental problem in applications like VLSI design, high-performance computing, etc. Nowadays, large-scale hypergraphs…”
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LearnGraph: A Learning-Based Architecture for Dynamic Graph Processing by Zhang, Lingling, Wu, Yijian, Jiang, Hong, Zhou, Ziyu, Lu, Tiancheng

“…Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and…”
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