Gotowe bibliografie tematyczne / Weisfeiler-Lehman graph isomorphism

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Gotowa bibliografia na temat „Weisfeiler-Lehman graph isomorphism”

Autor: Grafiati
Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Weisfeiler-Lehman graph isomorphism"

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Schulz, Till, Pascal Welke, and Stefan Wrobel. "Graph Filtration Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8196–203. http://dx.doi.org/10.1609/aaai.v36i8.20793.

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The majority of popular graph kernels is based on the concept of Haussler's R-convolution kernel and defines graph similarities in terms of mutual substructures. In this work, we enrich these similarity measures by considering graph filtrations: Using meaningful orders on the set of edges, which allow to construct a sequence of nested graphs, we can consider a graph at multiple granularities. A key concept of our approach is to track graph features over the course of such graph resolutions. Rather than to simply compare frequencies of features in graphs, this allows for their comparison in ter
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Yang, Sihong, Dezhi Jin, Jun Liu, and Ye He. "Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study." Brain Sciences 12, no. 7 (2022): 883. http://dx.doi.org/10.3390/brainsci12070883.

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Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the in
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Feng, Aosong, Chenyu You, Shiqiang Wang, and Leandros Tassiulas. "KerGNNs: Interpretable Graph Neural Networks with Graph Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6614–22. http://dx.doi.org/10.1609/aaai.v36i6.20615.

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Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted combinatorial features of graphs. In recent years, graph neural networks (GNNs) have become the state-of-the-art method in downstream graph-related tasks due to their superior performance. Most GNNs are based on Message Passing Neural Network (MPNN) frameworks. However, recent studies show that MPNNs can not exceed the power of the Weisfeiler-Lehman (WL) algorithm in graph isomorphism test. To address the limitations of
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Wu, Jun, Jingrui He, and Elizabeth Ainsworth. "Non-IID Transfer Learning on Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10342–50. http://dx.doi.org/10.1609/aaai.v37i9.26231.

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Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The cru
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You, Jiaxuan, Jonathan M. Gomes-Selman, Rex Ying, and Jure Leskovec. "Identity-aware Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10737–45. http://dx.doi.org/10.1609/aaai.v35i12.17283.

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Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL test. ID-GNN offers a minimal but powerful solution to limitat
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GROHE, MARTIN, and MARTIN OTTO. "PEBBLE GAMES AND LINEAR EQUATIONS." Journal of Symbolic Logic 80, no. 3 (2015): 797–844. http://dx.doi.org/10.1017/jsl.2015.28.

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AbstractWe give a new, simplified and detailed account of the correspondence between levels of the Sherali–Adams relaxation of graph isomorphism and levels of pebble-game equivalence with counting (higher-dimensional Weisfeiler–Lehman colour refinement). The correspondence between basic colour refinement and fractional isomorphism, due to Tinhofer [22; 23] and Ramana, Scheinerman and Ullman [17], is re-interpreted as the base level of Sherali–Adams and generalised to higher levels in this sense by Atserias and Maneva [1] and Malkin [14], who prove that the two resulting hierarchies interleave.
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Evdokimov, Sergei, and Ilia Ponomarenko. "On Highly Closed Cellular Algebras and Highly Closed Isomorphisms." Electronic Journal of Combinatorics 6, no. 1 (1998). http://dx.doi.org/10.37236/1450.

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We define and study $m$-closed cellular algebras (coherent configurations) and $m$-isomorphisms of cellular algebras which can be regarded as $m$th approximations of Schurian algebras (i.e. the centralizer algebras of permutation groups) and of strong isomorphisms (i.e. bijections of the point sets taking one algebra to the other) respectively. If $m=1$ we come to arbitrary cellular algebras and their weak isomorphisms (i.e. matrix algebra isomorphisms preserving the Hadamard multiplication). On the other hand, the algebras which are $m$-closed for all $m\ge 1$ are exactly Schurian ones wherea
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Rozprawy doktorskie na temat "Weisfeiler-Lehman graph isomorphism"

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Doshi, Siddhanth Rahul. "Graph Neural Networks with Parallel Local Neighborhood Aggregations." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5762.

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Graph neural networks (GNNs) have become very popular for processing and analyzing graph-structured data in the last few years. Using message passing as their basic building blocks that aggregate information from neighborhoods, GNN architectures learn low-dimensional graph-level or node-level embeddings useful for several downstream machine learning tasks. In this thesis, we focus on GNN architectures that perform parallel neighborhood aggregations (in short, referred to as PA-GNNs) for two tasks, namely, graph classification and link prediction. Such architectures have a natural advantage of
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Części książek na temat "Weisfeiler-Lehman graph isomorphism"

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Da San Martino, Giovanni, Nicolò Navarin, and Alessandro Sperduti. "Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions." In Neural Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_12.

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