IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning

By Kaspar Riesen, Horst Bunke. 2008

618 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

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Citations in the corpus

Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, background): “7, 8, 9, and 10, for the PTC, WEB, NCI109, and REDDIT12K datasets used in the main paper.”
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (2018, methodology): “GED has been widely used in many applications, such as graph similarity search [31, 51, 55, 56, 58], graph classi cation [40, 41], handwriting recognition [12], image indexing [52], etc.”
Graph Contrastive Learning Automated (2021, methodology): “We use datasets of diverse nature from the benchmark TUDataset (Morris et al., 2020), including graph data for small molecules & proteins (Riesen & Bunke, 2008; Dobson & Doig, 2003), computer vision (Nene et al., 1996) and various relation networks (Yanardag & Vishwanathan, 2015; Rozemberczki et…”
Discriminative prototype selection methods for graph embedding (2013, methodology): “For extensive testing of the proposed approaches, we have chosen a total of 10 different graph datasets from the publicly available IAM graph database repository [42].”
TUDataset: A collection of benchmark datasets for learning with graphs (2020, methodology): “Our collection provides several datasets originating from the IAM G RAPH D ATABASE (Riesen & Bunke, 2008) such as L ETTER and F INGERPRINT .”
Graph Convolutional Networks with EigenPooling (2019, background): “, ENZYMES [3, 36], PROTEINS [3, 10], and D&D [10, 37]; one mutagen data setMutagenicity [20, 31] (We denoted as MUTAG in Table 1 and Table 2); and two data sets that consist of chemical compounds screened for activity against non-small cell lung cancer and ovarian cancer cell lines, NCI1 and NCI109 ”
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation (2022, methodology): “For unsupervised and semi-supervised learning, we use datasets from the benchmark TUDataset [27], including graph data for various social networks [2, 52] and biochemical molecules [9, 32].”
Large Language Models on Graphs: A Comprehensive Survey (2023, background): “Graphs are prevalent data objects in scientific disciplines such as cheminformatics [183], [194], [200], material informatics [181], bioinformatics [201], and computer vision [147].”
Learning Discriminative Fisher Kernels (2011, methodology): “The mutagenicity data set comprises 4, 337 molecules, which are represented as graphs in which each node corresponds to one of fourteen atoms (i.e., there is a single discrete feature per node) and each edge corresponds to a covalent bond (Riesen & Bunke, 2008).”
Parameterized Explainer for Graph Neural Network (2020, methodology): “-life dataset, MUTAG, for graph classiﬁcation, which is also used in previous work [53]. It consists of 4,337 molecule graphs. Each graph is assigned to one of 2 classes based on its mutagenic effect [53, 40]. As discussed in [53, 9], carbon rings with chemical groups NH 2 or NO 2 2https://github.co”
Graph Matching Networks for Learning the Similarity of Graph Structured Objects (2019, methodology): “We focus on the function similarity search task, and also conduct experiments on an extra COIL-DEL mesh graph dataset (Riesen & Bunke, 2008), which contains 100 classes of mesh graphs corresponding to 100 types of objects.”
Adversarial Attack and Defense on Graph Data: A Survey (2018, neutral): “Besides the node-level tasks, Bitcoin, AIDS, and DHFR datasets which contain multiple graphs are used to investiagte the robustness of graph classﬁcation models [56], [113], [141], [165].”
An Exact Graph Edit Distance Algorithm for Solving Pattern Recognition Problems (2015, background): “We shed light on the IAM graph repository which is a widely used repository dedicated to a wide spectrum of tasks in pattern recognition and machine learning (Riesen and Bunke, 2008).”