Link Prediction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

80 leaderboard tables shown for this task, 80 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 80 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

67 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 67 until expanded.

Subtasks archive 2025-07-28

6 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 974 papers with code (1,949 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Attention Is All You Need
  12 Jun 2017 595 repositories listed Syntology ran 600 of 946 samples · 346 unverified · 451 pointer-only (licence)
  The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
• Graph Attention Networks
  30 Oct 2017 93 repositories listed Syntology ran 50 of 106 samples · 56 unverified · 43 pointer-only (licence)
  We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph…
• Modeling Relational Data with Graph Convolutional Networks
  17 Mar 2017 27 repositories listed Syntology ran 10 of 32 samples · 22 unverified · 15 pointer-only (licence)
  We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
• Variational Graph Auto-Encoders
  21 Nov 2016 22 repositories listed Syntology ran 7 of 13 samples · 6 unverified
  We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).
• Neural Graph Collaborative Filtering
  20 May 2019 21 repositories listed Syntology ran 6 of 7 samples · 1 unverified
  Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
• Inductive Representation Learning on Large Graphs
  7 Jun 2017 20 repositories listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.
• node2vec: Scalable Feature Learning for Networks
  3 Jul 2016 20 repositories listed Syntology ran 8 of 25 samples · 17 unverified · 3 pointer-only (licence)
  Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
• Graph Convolutional Matrix Completion
  7 Jun 2017 17 repositories listed
  We consider matrix completion for recommender systems from the point of view of link prediction on graphs.
• Benchmarking Graph Neural Networks
  2 Mar 2020 15 repositories listed Syntology ran 1 of 23 samples · 22 unverified
  In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
• Hierarchical Graph Representation Learning with Differentiable Pooling
  22 Jun 2018 14 repositories listed Syntology ran 1 of 20 samples · 19 unverified
  Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node…
• GNNExplainer: Generating Explanations for Graph Neural Networks
  10 Mar 2019 12 repositories listed Syntology ran 1 of 22 samples · 21 unverified
  We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
• RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
  26 Feb 2019 10 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
• EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
  26 Feb 2019 10 repositories listed Syntology ran 3 of 11 samples · 8 unverified · 1 pointer-only (licence)
  Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
• Link Prediction Based on Graph Neural Networks
  27 Feb 2018 10 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs.
• Embedding Entities and Relations for Learning and Inference in Knowledge Bases
  20 Dec 2014 10 repositories listed
  We consider learning representations of entities and relations in KBs using the neural-embedding approach.
• Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
  21 Nov 2019 9 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 1 pointer-only (licence)
  HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.
• Hyperspherical Variational Auto-Encoders
  3 Apr 2018 9 repositories listed Syntology ran 0 of 14 samples · 14 unverified
  But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization…
• Complex Embeddings for Simple Link Prediction
  20 Jun 2016 9 repositories listed
  In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.
• LINE: Large-scale Information Network Embedding
  12 Mar 2015 9 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
• How Attentive are Graph Attention Networks?
  30 May 2021 8 repositories listed Syntology ran 6 of 16 samples · 10 unverified · 1 pointer-only (licence)
  Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.
• Knowledge Graph Convolutional Networks for Recommender Systems
  18 Mar 2019 8 repositories listed Syntology ran 1 of 5 samples · 4 unverified · 1 pointer-only (licence)
  To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these…
• Convolutional 2D Knowledge Graph Embeddings
  5 Jul 2017 8 repositories listed
  In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.
• Translating Embeddings for Modeling Multi-relational Data
  1 Dec 2013 8 repositories listed
  We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
  17 Mar 2021 6 repositories listed
  Enabling effective and efficient machine learning (ML) over large-scale graph data (e.
• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
  20 May 2019 6 repositories listed
  Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.
• NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
  16 Dec 2018 6 repositories listed
  Negative sampling, which samples negative triplets from non-observed ones in the training data, is an important step in KG embedding.
• Neural Factorization Machines for Sparse Predictive Analytics
  16 Aug 2017 6 repositories listed
  However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data.
• Structural Deep Network Embedding
  1 Jun 2016 6 repositories listed
  Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.
• Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks
  13 Aug 2023 5 repositories listed
  These findings underscore the efficacy of the proposed loss functions in dynamic network modeling.
• Inductive Representation Learning on Temporal Graphs
  19 Feb 2020 5 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

Syntology lines on 19 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.