Graph Embedding

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 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

12 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

5 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 533 papers with code (1,192 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.

• 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…
• Inductive Relation Prediction by Subgraph Reasoning
  16 Nov 2019 10 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.
• 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.
• 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.
• Poincaré Embeddings for Learning Hierarchical Representations
  22 May 2017 9 repositories listed Syntology ran 6 of 14 samples · 8 unverified · 6 pointer-only (licence)
  Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.
• 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.
• GraphSAINT: Graph Sampling Based Inductive Learning Method
  10 Jul 2019 8 repositories listed
  Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
• Learning Combinatorial Optimization Algorithms over Graphs
  5 Apr 2017 8 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.
• 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.
• graph2vec: Learning Distributed Representations of Graphs
  17 Jul 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
• Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning
  28 Jul 2023 5 repositories listed
  Additionally, the layer depth in QAOA correlates to the number of decoding belief propagation iterations in the Wiberg decoding tree.
• 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.
• struc2vec: Learning Node Representations from Structural Identity
  11 Apr 2017 5 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Implementation and experiments of graph embedding algorithms.
• Multi-Paradigm Analysis of Thai Capital Market Linkages: Bivariate/Vine Copulas, Granger Causality, Network Centrality, and Graph Neural Network/Graph Embedding Approaches
  23 Feb 2023 4 repositories listed
  Analytically thorough understanding of causal, probabilistic, and informational linkages amongst modern, highly-interconnected capital markets is fundamental to the promotion of capital-market innovation, efficiency,…
• MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
  30 Sep 2022 4 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs.
• Multi-Relational Embedding for Knowledge Graph Representation and Analysis
  28 Sep 2020 4 repositories listed
  The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational…
• Fast Sequence-Based Embedding with Diffusion Graphs
  21 Jan 2020 4 repositories listed
  A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.
• Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
  17 Nov 2019 4 repositories listed
  In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along…
• Composition-based Multi-Relational Graph Convolutional Networks
  8 Nov 2019 4 repositories listed Syntology ran 2 of 6 samples · 4 unverified · 3 pointer-only (licence)
  Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
• Knowledge Graph Embedding for Ecotoxicological Effect Prediction
  2 Jul 2019 4 repositories listed
  A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity.
• Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
  12 Jun 2019 4 repositories listed Syntology ran 4 of 11 samples · 7 unverified
  Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
• Representation Learning for Attributed Multiplex Heterogeneous Network
  5 May 2019 4 repositories listed Syntology ran 0 of 6 samples · 6 unverified · 1 pointer-only (licence)
  Network embedding (or graph embedding) has been widely used in many real-world applications.
• Adversarially Regularized Graph Autoencoder for Graph Embedding
  13 Feb 2018 4 repositories listed
  Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
• Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
  28 Oct 2023 3 repositories listed Syntology ran 11 of 12 samples · 1 unverified
  However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also limits its…
• One-Hot Graph Encoder Embedding
  27 Sep 2021 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding.
• Bilinear Scoring Function Search for Knowledge Graph Learning
  1 Jul 2021 3 repositories listed
  We first set up a search space for AutoBLM by analyzing existing scoring functions.
• Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
  22 Apr 2021 3 repositories listed
  The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature.
• CoKE: Contextualized Knowledge Graph Embedding
  6 Nov 2019 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.
• Attributed Graph Clustering: A Deep Attentional Embedding Approach
  15 Jun 2019 3 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 1 pointer-only (licence)
  Graph clustering is a fundamental task which discovers communities or groups in networks.
• GLEE: Geometric Laplacian Eigenmap Embedding
  23 May 2019 3 repositories listed
  Graph embedding seeks to build a low-dimensional representation of a graph G.

Syntology lines on 18 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.