# word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data

@article{Grohe2020word2vecNG, title={word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data}, author={Martin Grohe}, journal={Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems}, year={2020} }

Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding…

## 62 Citations

### On the Surprising Behaviour of node2vec

- Computer ScienceArXiv
- 2022

This work focuses on node2vec, one of the most prominent graph embedding schemes, and analyses its embedding quality from multiple perspectives to indicate that embeddingquality is unstable with respect to parameter choices.

### ripple2vec: Node Embedding with Ripple Distance of Structures

- Computer ScienceData Sci. Eng.
- 2022

Experimental results on real datasets show that the results of the proposed node embedding method, named as ripple2vec, behave better than those of state-of-the-art methods, in node clustering and node classification, and are competitive to other methods in link prediction.

### Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

- Computer ScienceNeurIPS
- 2020

The experimental study confirms that the local algorithms, both kernel and neural architectures, lead to vastly reduced computation times, and prevent overfitting, and the kernel version establishes a new state-of-the-art for graph classification on a wide range of benchmark datasets.

### Exploiting Class Labels to Boost Performance on Embedding-based Text Classification

- Computer ScienceCIKM
- 2020

A weighting scheme, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency, category-exclusive words higher when computing word embeddings, leading to improved performance scores over the well-known weighting schemes TF-IDF and KLD as well as over the absence of a weighted scheme in most cases.

### Creativity Embedding: A Vector to Characterise and Classify Plausible Triples in Deep Learning NLP Models

- Computer ScienceCLiC-it
- 2020

The creativity embedding of a text based on four self-assessment creativity metrics, namely diversity, novelty, serendipity and magnitude, knowledge graphs, and neural networks, is defined.

### Dynamic Database Embeddings with FoRWaRD

- Computer ScienceArXiv
- 2021

FoRWaRD is comparable and sometimes superior to state-of-the-art embeddings in the static (traditional) setting and in the dynamic setting FoR WaRD outperforms the alternatives consistently and often considerably, and features only a mild reduction of quality even when the database consists of mostly newly inserted tuples.

### TF-CR: Weighting Embeddings for Text Classification

- Computer ScienceArXiv
- 2020

A novel weighting scheme is introduced, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency, category-exclusive words higher when computing word embeddings, leading to improved performance scores over existing weighting schemes, with a performance gap that increases as the size of the training data grows.

### Network representation learning based on social similarities

- Computer ScienceFrontiers in Environmental Science
- 2022

This paper investigates a novel social similarity-based method for learning network representations that is able to maintain both structural similarity of nodes and domain similarity and outperforms the state-of-the-art solutions.

### Scaling up graph homomorphism for classification via sampling

- Computer ScienceArXiv
- 2021

This paper proposes a high-performance implementation of a simple sampling algorithm which computes additive approximations of homomorphism densities and shows in experiments on synthetic data that this algorithm scales to very large graphs when implemented with Bloom filters.

### Graph Homomorphism Features: Why Not Sample?

- Computer Science, MathematicsPKDD/ECML Workshops
- 2021

This work-in-progress paper attempts to make this methodology scalable by obtaining additive approximations to graph homomorphism densities via a simple sampling algorithm, and shows in experiments that these approximate homomorphISM densities perform as well as homomorphicism numbers on standard graph classification datasets.

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