Introduction

We are studying the heterogeneous network integration problem. We have collected data from different social networking site. The dataset published in this page consists of two collections of social networks, where the networks within a collection are overlapped with each other (i.e. have users corresponding to the same real world person).

SNS network collection

The SNS data collection consists of five popular online social networking sites: Twitter, LiveJournal, Flickr, Last.fm, and MySpace.

Twitter: Twitter is a widely used social networking and micro-blogging site. Our dataset is obtained from [3], according to which it was crawled from Twitter's website. You can download the dataset from here.

LiveJournal: Livejournal is a free on-line social network where users can keep a blog, journal or diary. Our dataset is crawled from its website in late 2013, which contains 3,017,286 users and 87,037,567 links. Here defines link as the friend relationship, that is,two users are linked if one exists in the other's friends list. You can download the dataset from here.

Flickr: Flickr is a popular photo sharing network for users to upload photos and share photos. The Flickr dataset is crawled in early 2014, which consists of 215,495 individual users and 9,114,557 links. Like LiveJournal, the links here reflect friend relationships among users. You can download the dataset from here.

Last.fm: Last.fm provides a streaming radio service, where users can search music and get personalized recommendation. Last.fm builds detailed profile of users' musical taste and preference, which is the foundation for music recommendation. We crawled Last.fm in late 2013 and obtained a network that contains 136,420 users and 1,685,524 following links among them. You can download the dataset from here.

MySpace: MySpace is a social networking website which also has a strong music emphasis. The dataset we obtained from MySpace contains 854,498 user profiles as well as 6,489,736 directed connections among users. To reconstruct the network of these users, we treat connections as undirected and parallel links are combined into one. You can download the dataset from here.

Academia network collection

The Academia data collection consists of three academic or professional social networks: ArnetMiner (AM), Linkedin and Videolectures.

ArnetMiner: ArnetMiner is an expertise search and mining service in Computer Science domain. We obtained the entire ArnetMiner data up to the year 2013, including user profiles and co-author relationships. To construct co-author network, we create an undirected edge between two authors if they have coauthored a paper. This way we obtain a co-author network with more than 1 million nodes and nearly 4 million edges. You can download the dataset from here.

Linkedin: Linkedin is the world's largest professional network where users can maintain their profile page and connections. We collected approximately 3 million public profiles from Linkedin. The links between profiles are observed if two profiles are co-viewed by others . Hence we could construct an undirected network based on these "co-view" links. You can download the dataset from here.

Grouth Truth

SNS network collection:

The group truth mapping of SNS network collections was originally collected by Perito el. al [1,2] through Google Profiles service. Please contact the original owner to obtain the data. Here, we provide a subset of the data for evaluation.

Academia network collection:

The ground truth for Academia dataset is obtained through a crowdsourcing service on ArnetMiner. On each researcher's ArnetMiner profile, users can fill in urls linking to the external accounts. This service has been running on-line for more than one year and more than 10,000 interlinks record has been collected. Here, we provide a subset of the data for evaluation.

AMiner-Linkedin

We have submitted a paper about social network integration [4] to KDD2014, the manuscript can be found on http://arxiv.org/abs/1311.2670

Reference

[1] T. Chen, M. A. Kaafar, A. Friedman, and R. Boreli. Is more always merrier?: a deep dive into online social footprints. In 2012 ACM workshop on Workshop on online social networks, pages 67–72, 2012.

[2] D. Perito, C. Castelluccia, M. A. Kaafar, and P. Manils. How unique and traceable are usernames? In Privacy Enhancing Technologies, pages 1–17. Springer, 2011.

[3] H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In WWW ’10: Proceedings of the 19th international conference on World wide web, pages 591–600, New York, NY, USA, 2010. ACM.

[4] http://arxiv.org/abs/1311.2670

Heterogeneous Network Integration

Introduction