All the World's a (Hyper)Graph: A Data Drama
- URL: http://arxiv.org/abs/2206.08225v3
- Date: Wed, 6 Dec 2023 08:57:52 GMT
- Title: All the World's a (Hyper)Graph: A Data Drama
- Authors: Corinna Coupette, Jilles Vreeken, Bastian Rieck
- Abstract summary: Hyperbard is a dataset of diverse data representations from Shakespeare's plays.
Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication settings.
As an homage to our data source, and asserting that science can also be art, we present all our points in the form of a play.
- Score: 55.144729234861316
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce Hyperbard, a dataset of diverse relational data representations
derived from Shakespeare's plays. Our representations range from simple graphs
capturing character co-occurrence in single scenes to hypergraphs encoding
complex communication settings and character contributions as hyperedges with
edge-specific node weights. By making multiple intuitive representations
readily available for experimentation, we facilitate rigorous representation
robustness checks in graph learning, graph mining, and network analysis,
highlighting the advantages and drawbacks of specific representations.
Leveraging the data released in Hyperbard, we demonstrate that many solutions
to popular graph mining problems are highly dependent on the representation
choice, thus calling current graph curation practices into question. As an
homage to our data source, and asserting that science can also be art, we
present all our points in the form of a play.
Related papers
- G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
We develop a flexible question-answering framework targeting real-world textual graphs.
We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs.
G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
arXiv Detail & Related papers (2024-02-12T13:13:04Z) - When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding
and Reasoning [54.84870836443311]
The paper presents a new paradigm for understanding and reasoning about graph data by integrating image encoding and multimodal technologies.
This approach enables the comprehension of graph data through an instruction-response format, utilizing GPT-4V's advanced capabilities.
The study evaluates this paradigm on various graph types, highlighting the model's strengths and weaknesses, particularly in Chinese OCR performance and complex reasoning tasks.
arXiv Detail & Related papers (2023-12-16T08:14:11Z) - Universe Points Representation Learning for Partial Multi-Graph Matching [17.46692880231195]
We study the more general partial matching problem with multi-graph cycle consistency guarantees.
We propose a novel data-driven method for partial multi-graph matching, which uses an object-to-universe formulation and learns latent representations of abstract universe points.
arXiv Detail & Related papers (2022-12-01T18:58:26Z) - Hypergraph Convolutional Networks via Equivalency between Hypergraphs
and Undirected Graphs [59.71134113268709]
We present General Hypergraph Spectral Convolution(GHSC), a general learning framework that can handle EDVW and EIVW hypergraphs.
In this paper, we show that the proposed framework can achieve state-of-the-art performance.
Experiments from various domains including social network analysis, visual objective classification, protein learning demonstrate that the proposed framework can achieve state-of-the-art performance.
arXiv Detail & Related papers (2022-03-31T10:46:47Z) - Multi-Level Graph Contrastive Learning [38.022118893733804]
We propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs.
The original graph is first-order approximation structure and contains uncertainty or error, while the $k$NN graph generated by encoding features preserves high-order proximity.
Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven datasets.
arXiv Detail & Related papers (2021-07-06T14:24:43Z) - HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs [24.737560790401314]
We present HyperSAGE, a novel hypergraph learning framework that uses a two-level neural message passing strategy to accurately and efficiently propagate information through hypergraphs.
We show that HyperSAGE outperforms state-of-the-art hypergraph learning methods on representative benchmark datasets.
arXiv Detail & Related papers (2020-10-09T13:28:06Z) - Sub-graph Contrast for Scalable Self-Supervised Graph Representation
Learning [21.0019144298605]
Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs.
textscSubg-Con is proposed by utilizing the strong correlation between central nodes and their sampled subgraphs to capture regional structure information.
Compared with existing graph representation learning approaches, textscSubg-Con has prominent performance advantages in weaker supervision requirements, model learning scalability, and parallelization.
arXiv Detail & Related papers (2020-09-22T01:58:19Z) - Graph Pooling with Node Proximity for Hierarchical Representation
Learning [80.62181998314547]
We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology.
Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
arXiv Detail & Related papers (2020-06-19T13:09:44Z) - Unsupervised Graph Representation by Periphery and Hierarchical
Information Maximization [18.7475578342125]
Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vector space.
For the entire graph representation, most of existing graph neural networks are trained on a graph classification loss in a supervised way.
We propose an unsupervised graph neural network to generate a vector representation of an entire graph in this paper.
arXiv Detail & Related papers (2020-06-08T15:50:40Z) - Deep Learning for Learning Graph Representations [58.649784596090385]
Mining graph data has become a popular research topic in computer science.
The huge amount of network data has posed great challenges for efficient analysis.
This motivates the advent of graph representation which maps the graph into a low-dimension vector space.
arXiv Detail & Related papers (2020-01-02T02:13:28Z)
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.