The Web-Stanford dataset is a large-scale directed graph representing hyperlinks between web pages from the Stanford University domain. Each node represents a webpage, while each directed edge represents a hyperlink from one webpage to another.

This dataset is one of the most widely used benchmark datasets in graph analytics and network science. It is suitable for implementing PageRank, graph traversal algorithms, centrality measures, community detection, graph neural networks, and distributed graph processing.

Researchers, students, and developers can use this dataset to study real-world network structures, benchmark graph algorithms, and experiment with scalable graph processing frameworks.

The dataset is provided in a simple edge list format, making it compatible with popular graph libraries such as NetworkX, igraph, SNAP, graph-tool, Neo4j, PyTorch Geometric, and Deep Graph Library (DGL).

Whether you are learning graph theory or building large-scale graph analytics applications, the Web-Stanford dataset provides a realistic and well-established benchmark for experimentation.

• Hyperlinks between subreddits on Reddit
• Comments, questions, and answers on Stack Overflow
• Comments, questions, and answers on Math Overflow
• Comments, questions, and answers on Super User
• Comments, questions, and answers on Ask Ubuntu
• Users editing talk pages on Wikipedia
• E-mails between users at a research institution
• Messages on a Facebook-like platform at UC-Irvine
• Bitcoin OTC web of trust network
• Bitcoin Alpha web of trust network
• Student actions on a MOOC platform, with student drop-out binary labels.
• Dynamic face-to-face interaction network between group of people

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#temporal

This dataset contains complementary data to the paper "A Hybrid Matheuristic for the Spread of Influence on Social Networks" [1], which proposes a matheuristic for combinatorial optimization problems involving the spread of information in social networks. For the computational experiments discussed in that paper, we provide: - Two sets of instances, originally obtained from [2-6]; - The solutions attained by exact and heuristic methods; - The collected results; - The matheuristic source code; The directories "benchmark_*/instances/" contain files that describe the sets of instances. Each instance is associated with a graph containing {n} vertices and {m} edges. The first {m} lines of each file contain: {u} {v} where {u} and {v} identify a pair of vertices that determines an undirected edge. The next line contains {n} integers corresponding to the costs of the vertices. The last line contains {n} integers corresponding to the thresholds of the vertices. The directories "benchmark_*/solutions_*/" contain files describing feasible solutions for the corresponding sets of instances. The first line of each file contains: {s} where {s} is the number of vertices in the target set. Each of the next {s} lines contains: {v} where {v} identifies a target. The last line contains an integer that represents the target set cost. The directory "hmf_source_code/" contains an implementation of the matheuristic framework proposed in [1], namely, HMF. This work was supported by grants from Santander Bank, the Brazilian National Council for Scientific and Technological Development (CNPq), the São Paulo Research Foundation (FAPESP), the Fund for Support to Teaching, Research and Outreach Activities (FAEPEX), and the Coordination for the Improvement of Higher Education Personnel (CAPES), all in Brazil. Caveat: The opinions, hypotheses and conclusions or recommendations expressed in this material are the sole responsibility of the authors and do not necessarily reflect the views of Santander, CNPq, FAPESP, FAEPEX, or CAPES. References [1] F. C. Pereira, P. J. de Rezende, and T. Yunes. A Hybrid Matheuristic for the Spread of Influence on Social Networks. 2024. Submitted. [2] S. Raghavan and R. Zhang. A branch-and-cut approach for the weighted target set selection problem on social networks. 2024. https://doi.org/10.1287/ijoo.2019.0012 [3] J. Leskovec and A. Krevl. SNAP Datasets: Stanford Large Network Dataset Collection. 2024. https://snap.stanford.edu/data [4] R. A. Rossi and N. K. Ahmed. The Network Data Repository with Interactive Graph Analytics and Visualization. 2022. https://networkrepository.com [5] J. Kunegis. KONECT – The Koblenz Network Collection. 2013. http://dl.acm.org/citation.cfm?id=2488173 [6] O. Lesser, L. Tenenboim-Chekina, L. Rokach, and Y. Elovici. Intruder or Welcome Friend: Inferring Group Membership in Online Social Networks. 2013. https://doi.org/10.1007/978-3-642-37210-0_40

This dataset contains complementary data to the paper "A Row Generation Algorithm for Finding Optimal Burning Sequences of Large Graphs" [1], which proposes an exact algorithm for the Graph Burning Problem, an NP-hard optimization problem that models a form of contagion diffusion on social networks.

Concerning the computational experiments discussed in that paper, we make available:

• Four sets of instances;
• The optimal (or best known) solutions obtained;
• The source code;
• An Appendix with additional details about the results.

The "delta" input sets include graphs that are real-world networks [1,2], while the "grid" input set contains graphs that are square grids.

The directories "delta_10K_instances", "delta_100K_instances", "delta_4M_instances" and "grid_instances" contain files that describe the sets of instances. The first two lines of each file contain:

where

• Purpose: The Autonomous systems dataset provided by Stanford Network Analysis Project (https://snap.stanford.edu/data/as-733.html) represents a network that contains a large number of nodes. This might not be adequate to researchers who want to experiment with smaller number of nodes. In addition, the original data set is not stored in a format readable by GraphStream library (https://graphstream-project.org/ ). Therefore we provide a java code based on GraphStream library that extracts smaller completely connected subgraph from the original dataset and stores it in a GraphStream friendly format.

• as20000102.txt: contains the graph that is provided by Autonomous systems dataset provided by Stanford Network Analysis Project (https://snap.stanford.edu/data/as-733.html). The file is provided here for convenience, but, It can also be downloaded from the previous link. If you download the original dataset from the previous link, then please make sure to remove the comments that starts with # symbol at the beginning of the file

• GraphGenerator.java: Our code that extracts number of nodes from the original dataset. Then, delay and bandwidth values are added to network edges. After that, the extracted graph is stored in a GraphStream readable format. However, the result is a graph that contains isolated subgraphs. The code utilizes GraphStream library (https://graphstream-project.org/ )

• ConnectedGraphs.java: Our code that uses the graph extracted by the "GraphGenerator", then extracts the largest completely connected subgraph from it. The result is also stored in a GraphStream readable format. The code utilizes GraphStream library (https://graphstream-project.org/ )

• Note 1: If you use this code in your research accompanied with the Autonomous systems dataset, then, in addition to citation provided by Mendeley, please add proper citations to the original Autonomous systems dataset by Stanford Network Analysis Project and add proper citation to the GraphStream library.

• Note 2: If you use this code in your research without using the Autonomous systems dataset, then, please make proper citation as provided by Mendeley. In addition, please add proper citation to GraphStream library.

1. The file includes four publicly available dataset files: congers_network dataset, Netscience dataset, email Eu core dataset, and Facebook dataset. It can be obtained through public websites [1] Stanford website: https://snap.stanford.edu/data/ And [2] Network Data Repository website: https://networkrepository.com/ The above datasets are all real network datasets, containing two columns of data indicating the existence of a relationship between two nodes. The specific description is: The congers_network dataset is based on the interactive network of members of the 117th United States Congress on Twitter, where nodes represent Congress members and edges represent forwarding, referencing, replying, or mentioning relationships between members to quantify the probability of information dissemination. The Netscience dataset is derived from a scientific collaboration network, where nodes represent scientists and edges represent collaborative relationships between scientists. It is used to simulate the dissemination and impact of information in the field of scientific research. The email Eu core dataset is based on email interactions between large European research institutions, where nodes represent members of the institution and edges represent at least one email exchange between members. The Facebook dataset is composed of "circles" (or "friend lists") from Facebook, where nodes represent users and edges represent social connections between users, reflecting the social relationships between users. 2. The file includes comparative data on the scope of truth dissemination. xlsx This data is the direct result generated from the calculation and analysis in the paper. Specifically, it includes the comparison data of the number of rumor seeds and the number of truth seeds on the diffusion range of truth under two thresholds.

Context

This large corpus can be used to train scientific paper summarization models that utilize citations, facilitating research in supervised methods.

Previous datasets for scientific document summarization are small with only several dozen articles. This dataset includes 1000 examples which is much larger than the prior works.

Content

I acquired this dataset from here in XML format. The CL-Scisumm project developed the first large-scale, human-annotated Scisumm dataset, ScisummNet. It provides over 1,000 papers in the ACL anthology network with their citation networks (e.g. citation sentences, citation counts) and their comprehensive, manual summaries.

The text column has every token of the research paper, and the summary column consists of summaries of the scientific paper.

Acknowledgements

This dataset is possible by the CL-Scisumm shared task, which has been organized since 2014 for papers in the computational linguistics and NLP domain.

Inspiration

This dataset should be trained with SOTA models and perform better than the model proposed by the SCisummNet.