Graph datasets for clustering

The CORA dataset consists of seven distinct categories of scientific papers. It comprises 2708 papers, with each paper represented as a node in the network. There are 5429 citation links, each representing a directed edge from one paper (node) to another, indicating a citation relationship. Each paper is represented by a 1433-dimensional feature vector, where each value is 0 or 1, indicating the absence or presence of specific words from a predefined dictionary. CITE is a citation network dataset consisting of papers from six distinct research categories: Agents, Artificial Intelligence (AI), Databases (DB), Information Retrieval (IR), Machine Learning (ML), and Human-Computer Interaction (HCI). The dataset comprises 3327 academic papers. Each paper is represented by a 3703-dimensional word vector, indicating the absence or presence of specific words from a predefined dictionary. Additionally, the dataset includes 4732 citation links between papers, reflecting the citation relationships among papers. The DBLP dataset is derived from the DBLP computer science bibliography and represents a co-authorship network. Each node corresponds to an author, and an edge between two nodes indicates that the corresponding authors have co-authored at least one paper together. It contains 4058 nodes and 3528 edges, with each author represented by an 334-dimensional feature vector that describes their research areas. The ACM dataset is a paper network, derived from the ACM database. It contains a total of 3025 papers categorized into three categories: database, wireless communication, and data mining. Each paper is represented by a 1870-dimensional vector based on the research area of the article. There is an edge between two papers if they are written by the same author.CORA数据集包含7个不同类别的学术论文，总计2708篇。每篇论文作为网络中的一个节点，数据集共包含5429条引用链接，每条链接均为一条有向边，代表从一篇论文（节点）指向另一篇论文的引用关系。每篇论文由一个1433维的特征向量表示，向量中每个元素取值为0或1，分别代表预定义词典中的特定词汇未出现或出现。CITE是一个引用网络数据集，其数据来源于6个不同研究类别的论文：智能体（Agents）、人工智能（Artificial Intelligence, AI）、数据库（Databases, DB）、信息检索（Information Retrieval, IR）、机器学习（Machine Learning, ML）以及人机交互（Human-Computer Interaction, HCI）。该数据集共计3327篇学术论文，每篇论文由一个3703维的词向量表示，用于表征预定义词典中的特定词汇的出现与否。此外，数据集包含4732条论文间的引用链接，用以反映论文间的引用关联关系。DBLP数据集源自DBLP计算机科学文献库，是一个合著者网络。其中每个节点对应一位作者，两个节点之间的边代表对应作者至少合作发表过一篇论文。该数据集包含4058个节点与3528条边，每位作者由一个334维的特征向量表示，该向量用于描述其研究领域。ACM数据集是一个源自ACM数据库的论文网络。该数据集共计3025篇论文，分为3个类别：数据库、无线通信与数据挖掘。每篇论文由一个基于其研究领域的1870维向量表示。若两篇论文由同一作者撰写，则二者之间存在一条边。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于图聚类的集合，包含四个子数据集：CORA、CITE、DBLP和ACM，涵盖了学术论文引用网络和作者合作网络。每个子数据集都提供节点、边和高维特征向量，支持聚类算法在学术领域的应用，例如基于关键词或研究领域的分类。数据集规模适中，节点数从2708到4058不等，特征向量维度最高达3703，适用于机器学习和数据挖掘研究。