Collective Classi!cation in Network Data

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

1,758 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): “…against the iterative classiﬁcation algorithm (ICA) proposed in Lu & Getoor (2003) in conjunction with two logistic regression classiﬁers, one for local node features alone and one for relational classiﬁcation using local features and an aggregation operator as described in Sen et al. (2008).”
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.”
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, methodology): “We used the popular Cora and PubMed [42] citation graphs as our datasets.”
Variational Graph Auto-Encoders (2016, methodology): “We demonstrate the ability of the VGAE and GAE models to learn meaningful latent embeddings on a link prediction task on several popular citation network datastets [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 ”
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): “E GO - 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 [39].”
Geometric Deep Learning: Going beyond Euclidean data (2016, methodology): “The main drawback of such approaches is their treatment of geometric data as [IN4] Citation network analysis example: The CORA citation network [89] 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): “We compare our methods with several state-of-the-art methods, including GCN with validation (GCN+V); GCN without validation (GCN-V); GCN with Chebyshev ﬁlter (Cheby) (Kipf and Welling 2017); label propagation using ParWalks (LP) (Wu et al. 2012); Planetoid (Yang, Cohen, and Salakhutdinov 2016); Deep”
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).”
Pitfalls of Graph Neural Network Evaluation (2018, methodology): “Datasets For our experiments, we used the four well-known citation network datasets: PubMed [Namata et al., 2012], CiteSeer and CORA from Sen et al. [2008], as well as the extended version of CORA from Bojchevski and Günnemann [2018], denoted as CORA-Full.”
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels (2017, methodology): “As second experiment, we address the problem of graph node classiﬁcation using the Cora citation graph [20].”
A Comprehensive Survey on Graph Neural Networks (2019, neutral): “…with the same meaning given a semantic graph of abstract ], [23], [25], [41], [43], [44], [45] [49], [50], [51], [53], [56], [61], [62] Citeseer [117] 1 3327 4732 3703 6 [22], [41], [43], [45], [50], [51], [53] [56], [61], [62] Pubmed [117] 1 19717 44338 500 3 [18], [22], [25], [41], [43],…”