Link Property Prediction

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 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.

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

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (13 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.

• 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.
• Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
  13 Jun 2021 3 repositories listed Syntology ran 2 of 15 samples · 13 unverified
  To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the…
• AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
  26 Apr 2019 3 repositories listed
  The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.
• Pairwise Learning for Neural Link Prediction
  6 Dec 2021 2 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.
• Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
  30 Oct 2020 2 repositories listed Syntology ran 2 of 7 samples · 5 unverified · 1 pointer-only (licence)
  In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link).
• Temporal graph models fail to capture global temporal dynamics
  27 Sep 2023 1 repository listed
  We outline our observations and propose a trivial optimization-free baseline of "recently popular nodes" outperforming other methods on medium and large-size datasets in the Temporal Graph Benchmark.
• Path-aware Siamese Graph Neural Network for Link Prediction
  10 Aug 2022 1 repository listed
  In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks.
• D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
  22 Dec 2021 1 repository listed
  Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs).
• VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
  27 Oct 2021 1 repository listed
  Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
• Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
  6 Oct 2021 1 repository listed
  Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC).
• Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities
  1 Jun 2021 1 repository listed
  Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs.
• Distance-Enhanced Graph Neural Network for Link Prediction
  20 May 2021 1 repository listed
  To overcome this difficulty, we propose an anchorbased distance: First, we randomly select K anchor vertices from the graph and then calculate the shortest distances of all vertices in the graph to them.

Syntology lines on 4 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.