Node Embeddings via Neighbor Embeddings

• URL: http://arxiv.org/abs/2503.23822v1
• Date: Mon, 31 Mar 2025 08:16:03 GMT
• Title: Node Embeddings via Neighbor Embeddings
• Authors: Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich, Andrew Draganov, Dmitry Kobak,
• Abstract summary: We introduce graph t-SNE and graph CNE, a contrastive neighbor embedding method that produces high-dimensional node representations.<n>We show that both graph t-SNE and graph CNE strongly outperform state-of-the-art algorithms in terms of local structure preservation.
• Score: 11.841966603069865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph layouts and node embeddings are two distinct paradigms for non-parametric graph representation learning. In the former, nodes are embedded into 2D space for visualization purposes. In the latter, nodes are embedded into a high-dimensional vector space for downstream processing. State-of-the-art algorithms for these two paradigms, force-directed layouts and random-walk-based contrastive learning (such as DeepWalk and node2vec), have little in common. In this work, we show that both paradigms can be approached with a single coherent framework based on established neighbor embedding methods. Specifically, we introduce graph t-SNE, a neighbor embedding method for two-dimensional graph layouts, and graph CNE, a contrastive neighbor embedding method that produces high-dimensional node representations by optimizing the InfoNCE objective. We show that both graph t-SNE and graph CNE strongly outperform state-of-the-art algorithms in terms of local structure preservation, while being conceptually simpler.

Related papers

• Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation [153.92387500677023]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The proposed graph Transformer encoder combines graph convolutions and self-attentions in a Transformer to model both local and global interactions. We also propose a novel self-guided pre-training method for graph representation learning.
  arXiv  Detail & Related papers  (2024-01-15T14:36:38Z)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• Graph Transformer GANs for Graph-Constrained House Generation [223.739067413952]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The GTGAN learns effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task.
  arXiv  Detail & Related papers  (2023-03-14T20:35:45Z)
• Local Intrinsic Dimensionality Measures for Graphs, with Applications to Graph Embeddings [1.1602089225841632]
  We propose NC-LID, a novel LID-related measure for quantifying the discriminatory power of the shortest-path distance with respect to natural communities of nodes as their intrinsic localities. It is shown how this measure can be used to design LID-aware graph embedding algorithms by formulating two LID-elastic variants of node2vec. Our empirical analysis of NC-LID on a large number of real-world graphs shows that this measure is able to point to nodes with high link reconstruction errors in node2vec embeddings better than node centrality metrics.
  arXiv  Detail & Related papers  (2022-08-25T10:32:07Z)
• Local Graph Embeddings Based on Neighbors Degree Frequency of Nodes [0.0]
  We propose a strategy for graph machine learning and network analysis by defining certain local features and vector representations of nodes. By extending the notion of the degree of a node via Breath-First Search, a general family of bf centrality functions is defined. We show that centrality and closeness can be learned by applying deep learning to these local features.
  arXiv  Detail & Related papers  (2022-07-30T07:07:30Z)
• Directed Graph Representation through Vector Cross Product [2.398608007786179]
  Graph embedding methods embed the nodes in a graph in low dimensional vector space while preserving graph topology. Recent work on directed graphs proposed to preserve the direction of edges among nodes by learning two embeddings, source and target, for every node. We propose a novel approach that takes advantage of the non commutative property of vector cross product to learn embeddings that inherently preserve the direction of edges among nodes.
  arXiv  Detail & Related papers  (2020-10-21T03:17:44Z)
• node2coords: Graph Representation Learning with Wasserstein Barycenters [59.07120857271367]
  We introduce node2coords, a representation learning algorithm for graphs. It learns simultaneously a low-dimensional space and coordinates for the nodes in that space. Experimental results demonstrate that the representations learned with node2coords are interpretable.
  arXiv  Detail & Related papers  (2020-07-31T13:14:25Z)
• GraphCL: Contrastive Self-Supervised Learning of Graph Representations [20.439666392958284]
  We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. In both transductive and inductive learning setups, we demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks.
  arXiv  Detail & Related papers  (2020-07-15T22:36:53Z)
• Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs [54.176285420428776]
  We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all nodes in the input graph - not only direct neighbors - facilitating the detection of global patterns. Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph.
  arXiv  Detail & Related papers  (2020-06-16T15:20:04Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)
• Dual Graph Representation Learning [20.03747654879028]
  Graph representation learning embeds nodes in large graphs as low-dimensional vectors. We present a context-aware unsupervised dual encoding framework, textbfCADE, to generate representations of nodes.
  arXiv  Detail & Related papers  (2020-02-25T04:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.