14 datasets found

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  • COLLAB

    The dataset used in the paper is a real-world dataset for future link prediction task on dynamic graphs.
  • ICEWS14 and ICEWS05-15

    Temporal Knowledge Graph (TKG) datasets for link prediction task
  • LiveJournal

    Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning.
  • Gplus

    Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning.
  • Facebook

    Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning.
  • Revisiting link prediction: a data perspective

    Revisiting link prediction: a data perspective.
  • Twitter and Foursquare

    The dataset used in the paper is Twitter and Foursquare, two real-world aligned heterogeneous social networks.
  • ConvE

    Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data.
  • DistMult

    Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data.
  • Cora, CiteSeer, PubMed, CS, Photo

    Citation networks (Cora, CiteSeer, PubMed) and coauthor network (CS) and co-purchase graph (Photo)
  • WN18RR

    Knowledge graphs store a wealth of knowledge from the real world into structured graphs, which consist of collections of triplets, and each triplet (h, r, t) represents that...
  • Flickr

    The dataset used in the paper is a multi-graph problem, where each graph represents a different network. The dataset is used to evaluate the performance of the RECS algorithm on...
  • BlogCatalog

    The BlogCatalog dataset is a blog directory that manages bloggers and their blogs. Each unit is a blogger and the features are bag-of-words representations of keywords in...
  • ACM

    The dataset used in the paper is a heterogeneous graph, which is a graph that contains multiple types of nodes and edges. The dataset is used for semi-supervised graph learning...