-
Flip-Flop Sublinear Models for Graphs
The dataset used in the paper is a graph dataset, where each graph is represented as a permutation group and a matrix space. The dataset is used to prove the existence of a... -
D&D, PROTEINS, and COLLAB datasets
D&D, PROTEINS, and COLLAB datasets for graph classification tasks -
Barabasi-Albert Graphs
The dataset used in this paper is a collection of Barabasi-Albert (BA) graphs with different preferential attachment factors (m) ranging from m = 1 (BA-1) to m = 6 (BA-6). -
Amazon clothing, Amazon-Electronics, and DBLP
The dataset used in the paper is Amazon clothing, Amazon-Electronics, and DBLP. -
Online Estimation of Multiple Dynamic Graphs in Pattern Sequences
The dataset used in the paper is a binary time-series data, where each time step is a binary pattern. The data is generated from a mixture of multiple graphs with time-dependent... -
Graph Datasets
The dataset used in this paper is a collection of real-world graph datasets from various domains, including biological networks, social networks, and collaboration networks. -
Learning Combinatorial Optimization Algorithms over Graphs
A dataset for learning combinatorial optimization algorithms over graphs. -
Delaunay Triangulation
The dataset used in the paper is a Delaunay Triangulation with n vertices, where n ranges from 1000 to 10000. -
Random Geometric Graphs
The dataset is a random geometric graph with vertex set [n] based on n i.i.d. random vectors X1,..., Xn drawn from an unknown density f on Rd. -
ER Random Graphs
The dataset used in the paper is ER random graphs, which are generated by the Erdos-Renyi random graph model. The graphs are used to test the proposed β-VAE model for... -
ER Graphs Dataset
The dataset used in the paper consists of 10 different ER graphs with varying densities. -
Cora, Citeseer, and Pubmed citation networks
The dataset used in the paper is citation networks, including Cora, Citeseer, and Pubmed. -
Peptide-func and Peptide-struct
The dataset used in the paper is a graph, where each node represents a node in the graph and each edge represents a relation between nodes.
