Collective Classification in Network Data

By Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, Tina Eliassi-Rad. 2008

4,707 citations recorded by the source corpus.

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In Graph Learning

Citations in the corpus

Semi-Supervised Classification with Graph Convolutional Networks (2016, methodology): “In the citation network datasets—Citeseer, Cora and Pubmed (Sen et al., 2008)—nodes are documents and edges are citation links.”
Graph Attention Networks (2017, methodology): “Both models are initialized using Glorot initialization (Glorot & Bengio, 2010) and trained to minimize cross-entropy on the training nodes using the Adam SGD optimizer (Kingma & Ba, 2014) with an initial learning rate of 0.01 for Pubmed, and 0.005 for all other datasets.”
Revisiting Semi-Supervised Learning with Graph Embeddings (2016, methodology): “1% on Pubmed, which indicates that our embedding techniques are more effective than graph Laplacian regularization.”
Diffusion-Convolutional Neural Networks (2015, background): “The Pubmed corpus [5] consists of 19,717 scientific papers from the Pubmed database on the subject of diabetes.”
DeepWalk: online learning of social representations (2014, background): “Exact inference in the collective classification problem is NP-hard, and solutions have focused on the use of approximate inference algorithm which may not be guaranteed to converge [37].”
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, methodology): “Vertex classiﬁcation accuracy on the Cora and PubMed datasets following the splitting suggested in [60].”
Variational Graph Auto-Encoders (2016, methodology): “Figure 1: Latent space of unsupervised VGAE model trained on Cora citation network dataset [1].”
Fast Graph Representation Learning with PyTorch Geometric (2019, background): “In detail, we provide over 60 graph kernel benchmark datasets 3 (Kersting et al., 2016), e.g. , PROTEINS or IMDB-BINARY, the citation graphs Cora, CiteSeer, PubMed and Cora-Full (Sen et al., 2008; Bojchevski & Günnemann, 2018), the Coauthor CS/Physics and Amazon Computers/Photo datasets from Shchur ”
Auto-GNN: Neural architecture search of graph neural networks (2019, methodology): “We utilize Cora, Citeseer and Pubmed [34] for the transductive learning, and use PPI for the inductive learning [7].”
Deep Graph Infomax (2018, methodology): “For the transductive learning tasks (Cora, Citeseer and Pubmed), our encoder is a one-layer Graph Convolutional Network (GCN) model (Kipf & Welling, 2016a), with the following propagation rule: where ˆ A = A + I N is the adjacency matrix with inserted self-loops and ˆ D is its corresponding degree m”
Representation Learning on Graphs with Jumping Knowledge Networks (2018, background): “Our experiments on Citeseer, Cora and Reddit are transductive.”
Graph Normalizing Flows (2019, methodology): “EGO-SMALL is a set of 200 graphs, where 4 ≤ |V | ≤ 18, drawn from the larger Citeseer network dataset [24].”
Graphite: Iterative Generative Modeling of Graphs (2018, methodology): “We compared across standard benchmark citation network datasets: Cora, Citeseer, and Pubmed with papers as nodes and citations as edges (Sen et al., 2008).”
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (2018, methodology): “757 3-hop ego networks extracted from the Citeseer network (Sen et al., 2008) with 50 ≤ |V | ≤ 399.”
Predict then Propagate: Graph Neural Networks meet Personalized PageRank (2018, methodology): “CITESEER (Sen et al., 2008), CORA-ML (McCallum et al.”
Lifelong Graph Learning (2020, neutral): “We perform comprehensive experiments on popular graph datasets including citation graph Cora, Citeseer, Pubmed, and ogbn-arXiv [28, 37].”
Geometric Deep Learning: Going beyond Euclidean data (2016, methodology): “We are [IN6] Citation network analysis application: The CORA citation network [90] is a graph containing 2708 vertices representing papers and 5429 edges representing citations.”
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018, methodology): “Spectral GCNNs deﬁne the convolution by decomposing a graph signal s ∈ R n (a scalar for each vertex) on the spectral domain and then applying a spectral ﬁlter g θ (a function of eigenvalues of L sym ) on the spectral components (Bruna et al. 2014; Sandryhaila and Moura 2013; Shuman et al. 2013).”
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks (2019, methodology): “We evaluate our approach on 8 transductive benchmark datasets: the tasks of classifying academic papers (Cora, CiteSeer, PubMed, Cora Full) (Sen et al., 2008; Bojchevski & Günnemann, 2018), active research fields of authors (Coauthor CS, Coauthor Physics) (Shchur et al., 2018) and product categorie”
Simplifying Graph Convolutional Networks (2019, methodology): “Figure 2 depicts the filtering operations associated with the normalized adjacency Sadj = D−1/2AD−1/2 and its augmented variant S̃adj = D̃−1/2ÃD̃−1/2 on the Cora dataset (Sen et al., 2008).”