Automating the Construction of Internet Portals with Machine Learning

By Andrew McCallum, Kamal Nigam, Jason D. M. Rennie, Kristie Seymore. 2000

1,670 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, ML Systems & Hardware

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, background): “3 Academic Citation Data The Cora dataset comprises a large collection of approximately 200,000 academic papers on computer science topics, including citation information [102].”
Predict then Propagate: Graph Neural Networks meet Personalized PageRank (2018, neutral): “, 2008), CORA-ML (McCallum et al., 2000; Bojchevski & Günnemann, 2018) and PUBMED (Namata et al.”
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling (2018, methodology): “…the original GCN model as well as Graph-SAGE, on the following benchmark tasks: (1) classifying research topics using the Cora citation data set (McCallum et al., 2000); (2) categorizing academic papers with the Pubmed database; and (3) predicting the community structure of a social network…”
Adversarial Attacks on Neural Networks for Graph Data (2018, background): “se them as the stopping criterion (i.e., stop when validation error increases). The labels of the unlabeled nodes are never visible to the surrogate model during training. Dataset N LCC E LCC Cora-ML [22] 2,810 7,981 CiteSeer [29] 2,110 3,757 Pol. Blogs [1] 1,222 16,714 Table 1: Dataset statistics. ”
NetGAN: Generating Graphs via Random Walks (2018, background): “Name NLCC ELCC Reference CORA-ML 2,810 7,981 (McCallum et al., 2000) CORA 18,800 64,529 (McCallum et al.”
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (2017, background): “Cora (McCallum et al., 2000) is a well-known citation network labeled based on the paper topic.”
Adversarial Network Embedding (2017, background): “Cora and Citeseer are paper citation networks constructed by (McCallum et al. 2000).”
Knowledge transfer via multiple model local structure mapping (2008, methodology): “[23] D.”
A Simple Relational Classifier (2003, methodology): “The first case study is based on the CoRA data set [17], which is a data set of academic papers within Computer Science.”
All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs (2020, neutral): “CoraML is the subset of machine learning papers from the well-know Cora dataset [23].”
Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking (2017, background): “Cora [16] is a well-known citation network of machine learning papers divided into classes based on the topic.”
Reference reconciliation in complex information spaces (2005, background): “For example, most of the work on reconciling publications for portals such as Citeseer [7] and Cora [25] has focused solely on the publications.”
Discovering Diverse and Salient Threads in Document Collections (2012, methodology): “To qualitatively illustrate our model, we apply it to Cora (McCallum et al., 2000).”
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification (2019, background): “See Appendix 6.2 for the results of other conﬁgurations of ( p, s ) . et al., 2000; Sen et al., 2008), CiteSeer (Giles et al., 1998; Sen et al., 2008), and PubMed(Sen et al., 2008) datasets for experiments.”
User Profiles for Personalized Information Access (2007, background): “The authors organize the library contents into a concept hierarchy of 97 classes [51], and manually provide 5-10 example documents for each concept.”
Rethinking Graph Neural Architecture Search from Message-passing (2021, background): “Recent work [5] points that existing benchmarks such as Cora [20], Citeseer [7] and TU [11] are too simple to distinguish the representation power of complex GNNs.”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, neutral): “The cora data set (McCallum et al., 2000) comprises computer science research papers.”
Accurate Information Extraction from Research Papers using Conditional Random Fields (2004, positive): “, 1999) andCora (McCallum et al., 2000), give researchers tremendous power and convenience in their research.”
Combining content and link for classification using matrix factorization (2007, methodology): “The two datasets are the WebKB data set[1] and the Cora data set [ 15 ].”