We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.
@cite_9 focus on the parallel crawling of OSNs; @cite_33 refer they used a parallel crawler written in Python and running on a cluster of 56 machines, but avoiding technical details.
@cite_9 @cite_33
2016589434 2137135938
Given a huge online social network, how do we retrieve information from it through crawling? Even better, how do we improve the crawling performance by using parallel crawlers that work independently? In this paper, we present the framework of parallel crawlers for online social networks, utilizing a centralized queue. To show how this works in practice, we describe our implementation of the crawlers for an online auction website. The crawlers work independently, therefore the failing of one crawler does not affect the others at all. The framework ensures that no redundant crawling would occur. Using the crawlers that we built, we visited a total of approximately 11 million auction users, about 66,000 of which were completely crawled. When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search. Usually, a citation's context contains explicit words explaining the citation. Using this, we propose a method that "translates" research papers into references. By considering the citations and their contexts from existing papers as parallel data written in two different "languages", we adopt the translation model to create a relationship between these two "vocabularies". Experiments on both CiteSeer and CiteULike dataset show that our approach outperforms other baseline methods and increase the precision, recall and f-measure by at least 5 to 10 , respectively. In addition, our approach runs much faster in the both training and recommending stage, which proves the effectiveness and the scalability of our work.