Stack overflow favorite bipartite temporal network

Published: 25 February 2018| Version 2 | DOI: 10.17632/8yj6m52m5j.2
Contributor:
Khushnood Abbas

Description

This is the bipartite Stack Overflow favorite network. Stack Overflow is the main question and answer website of the Stack Exchange Network. The nodes represent users and posts. An undirected, unweighted edge denotes that a user has marked a post as a favorite. More information about the network is provided here: http://konect.uni-koblenz.de/networks/stackexchange-stackoverflow Files: meta.stackexchange-stackoverflow -- Metadata about the network out.stackexchange-stackoverflow -- The adjacency matrix of the network in space separated values format, with one edge per line The meaning of the columns in out.stackexchange-stackoverflow are: First column: ID of from node Second column: ID of to node Fourth column: timestamp of the edge Complete documentation about the file format can be found in the KONECT handbook, in the section File Formats, available at: http://konect.uni-koblenz.de/publications All files are licensed under a Creative Commons Attribution-ShareAlike 2.0 Germany License. For more information concerning license visit http://konect.uni-koblenz.de/license. Use the following References for citation: @MISC{konect:2014:stackexchange-stackoverflow, title = {Stack Overflow network dataset -- {KONECT}}, month = oct, year = {2014}, url = {http://konect.uni-koblenz.de/networks/stackexchange-stackoverflow} } @misc{konect:stackexchange, title = {{Stack} {Exchange} {Data} {Explorer}}, author = {{Stack Exchange Inc.}}, year = {2011}, howpublished = {\url{http://data.stackexchange.com/}}, } @inproceedings{konect, title = {{KONECT} -- {The} {Koblenz} {Network} {Collection}}, author = {Jérôme Kunegis}, year = {2013}, booktitle = {Proc. Int. Conf. on World Wide Web Companion}, pages = {1343--1350}, url = {http://userpages.uni-koblenz.de/~kunegis/paper/kunegis-koblenz-network-collection.pdf}, url_presentation = {http://userpages.uni-koblenz.de/~kunegis/paper/kunegis-koblenz-network-collection.presentation.pdf}, }

Files

Steps to reproduce

Follow the same rule https://github.com/khushnood/Phd/blob/master/DataSets/YouTubeDataPreparation.R

Institutions

University of Electronic Science and Technology of China - Qingshuihe Campus

Categories

Data Science, Machine Learning, Big Data, Recommendation System

Licence