Summary of Dtgb: a Comprehensive Benchmark For Dynamic Text-attributed Graphs, by Jiasheng Zhang et al.
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
by Jiasheng Zhang, Jialin Chen, Menglin Yang, Aosong Feng, Shuang Liang, Jie Shao, Rex Ying
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Dynamic text-attributed graphs (DyTAGs) are increasingly relevant in real-world scenarios, where graph structures and text descriptions evolve over time. To facilitate research in this area, we introduce the Dynamic Text-attributed Graph Benchmark (DTGB), a collection of large-scale, time-evolving graphs from diverse domains, enriched by dynamically changing text attributes and categories. We design standardized evaluation procedures for four real-world use cases: future link prediction, destination node retrieval, edge classification, and textual relation generation. These tasks require models to understand both dynamic graph structures and natural language, highlighting the unique challenges posed by DyTAGs. We conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants using LLM embeddings, along with 6 powerful large language models (LLMs). Our results show the limitations of existing models in handling DyTAGs. The proposed DTGB fosters research on DyTAGs and their broad applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dynamic text-attributed graphs are a new type of graph that combines information from multiple sources to create a single, evolving graph. This paper introduces a new dataset called Dynamic Text-attributed Graph Benchmark (DTGB) that contains many examples of these types of graphs. The dataset is divided into four different tasks: predicting future connections in the graph, finding specific nodes or edges, classifying edges based on their meaning, and generating text that describes relationships between nodes. The paper also shows how well different computer programs can perform these tasks using this new dataset. This helps researchers understand what types of programs are best for working with dynamic text-attributed graphs. |
Keywords
* Artificial intelligence * Classification
