Sign In. Online social networking sites like YouTube are among the most popular sites on the Internet. Understanding these behavior of user sessions is important, both to improve current systems and to design new applications of online social networks. In this paper, we analyzed numerous traces generated by a ubiquitous measurement technique from an edge network perspective to assess the inter-transaction times characteristics experienced.
When viewed as time series data, inter-transaction times often exhibit long-range dependence manifested by Hurst parameter estimates greater than 0.5. Our results suggest that the inter-transaction times can be bursty across multiple time scales even at the microflow level, implying high performance variability during sufficiently long-lived application sessions. We anticipate that the quantification of such phenomena can enable applications to optimize and adjust their operation in care of potential performance degradation. Analysis of user behaviour as time series. Alan Dix, Janet Finlay and Russell Beale The trace of user interactions with a system is the primary source of data for on-line user modelling and for many design and research experiments.
This trace should really be analysed as a time series, but standard time series techniques do not deal well with discrete data and fuzzy matching. Techniques from machine learning (neural nets and inductive learning) have been applied to this analysis but these are limited to fixed size patterns and fail to deal properly with the trace as a time series. 1. The trace as data source System-logged traces of a user's interaction with an application are a primary source of data for automatic user modelling and for a wide range of research and evaluation experiments.
The second reason why traces are important is the ease with which they can be collected. Modelling users by example: advantages and applications User behaviour is complex but it is not arbitrary. Traces as time series. Pages.cpsc.ucalgary.ca/~zongpeng/publications/mmcn08.pdf. Zaitun Time Series 0.2.1 Download - Freeware Files.com - Home & Education Category. Www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15782-f06/slides/timeseries.pdf.
Ilpubs.stanford.edu:8090/984/1/paper-memeshapes.pdf. Sign In. Ucinet for Windows: Software for Social Network Analysis. Information revelation and privacy in online social networks. Analysis of topological characteristics of huge online social networking services. Measurement and analysis of online social networks. Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks.
This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network's graph.
Our results confirm the power-law, small-world, and scale-free properties of online social networks. Unveiling facebook. Online social networking sites such as Facebook and MySpace have become increasingly popular, with close to 500 million users as of August 2008. The introduction of the Facebook Developer Platform and OpenSocial allows third-party developers to launch their own applications for the existing massive user base. The viral growth of these social applications can potentially influence how content is produced and consumed in the future Internet. To gain a better understanding, we conducted a large-scale measurement study of the usage characteristics of online social network based applications. In particular, we developed and launched three Facebook applications, which have achieved a combined subscription base of over 8 million users.
Using the rich dataset gathered through these applications, we analyze the aggregate workload characteristics (including temporal and geographical distributions) as well as the structure of user interactions. User interactions in social networks and their implications. Analyzing patterns of user content generation in online social networks. Various online social networks (OSNs) have been developed rapidly on the Internet. Researchers have analyzed different properties of such OSNs, mainly focusing on the formation and evolution of the networks as well as the information propagation over the networks.
In knowledge-sharing OSNs, such as blogs and question answering systems, issues on how users participate in the network and how users "generate/contribute" knowledge are vital to the sustained and healthy growth of the networks. However, related discussions have not been reported in the research literature. In this work, we empirically study workloads from three popular knowledge-sharing OSNs, including a blog system, a social bookmark sharing network, and a question answering social network to examine these properties. Comparison of online social relations in volume vs interaction. Online social networking services are among the most popular Internet services according to Alexa.com and have become a key feature in many Internet services.
Users interact through various features of online social networking services: making friend relationships, sharing their photos, and writing comments. These friend relationships are expected to become a key to many other features in web services, such as recommendation engines, security measures, online search, and personalization issues. However, we have very limited knowledge on how much interaction actually takes place over friend relationships declared online.
A friend relationship only marks the beginning of online interaction. Does the interaction between users follow the declaration of friend relationship? We have analyzed structural characteristics of the activity network and compared them with the friends network. International ethnographic observation of social networking sites. A measurement-driven analysis of information propagation in the flickr social network. Online social networking sites like MySpace, Facebook, and Flickr have become a popular way to share and disseminate content. Their massive popularity has led to viral marketing techniques that attempt to spread content, products, and ideas on these sites. However, there is little data publicly available on viral propagation in the real world and few studies have characterized how information spreads over current online social networks. In this paper, we collect and analyze large-scale traces of information dissemination in the Flickr social network.
Our analysis, based on crawls of the favorite markings of 2.5 million users on 11 million photos, aims at answering three key questions: (a) how widely does information propagate in the social network? (b) how quickly does information propagate? And (c) what is the role of word-of-mouth exchanges between friends in the overall propagation of information in the network? Characterizing user behavior in online social networks. Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn.
The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Classification - Social network datasets - Statistical Analysis - Stack Exchange. Database dumps - database dump opendata.
Wikipedia makes its entire contents available in a (gargantuan) single downloadable file: the Wikipedia Dump. What are some other websites or public databases that do this? I am trying to convince the operator of a public information database to release its entire contents as a single file or archive so I (and others) can play with the data locally. They should be open to the idea since their mandate is to make these data available to everyone, but it would help if I could point to as many examples as possible of databases that let the public access their contents like this. P.S. Ripping the db myself is not an option. P.P.S. I know about the Mefi Infodump. Datasets Archive. Discover Yourself! Some Datasets Available on the Web » Data Wrangling Blog. SNAP: Network datasets: LiveJournal social network.
Large Network Dataset Collection. Linking Open Data.