1 / 20

A Connectivity-Based Popularity Prediction Approach for Social Networks

Predicting content popularity in social networks based on connectivity patterns to allocate server resources accurately and improve performance.

singletonk
Download Presentation

A Connectivity-Based Popularity Prediction Approach for Social Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Connectivity-Based Popularity Prediction Approach for Social Networks HuangmaoQuan, Ana Milicic, Slobodan Vucetic, and Jie Wu Department of Computer and Information Sciences Temple University

  2. Overview … Content 1 Content 2 … Server resources • Given a set of server resources? • How much server resources should we allocated to Content 1? How much to Content 2?

  3. Overview … Popular Less popular Content 1 Content 2 … Allocated to Content 1 Allocated to Content 2 • Popular content have higher traffic, so allocating more resources will improve performance • If we know that Content 1 will be more popular, we can allocate more resources accordingly

  4. Overview … • Accurately determining popularity has other applications • Determining advertising rates • Develop marketing strategies • Clearly, inaccurate prediction will result in worst performance

  5. Overview … • Accurately determining popularity has other applications • Determining advertising rates • Develop marketing strategies • Clearly, inaccurate prediction will result in worst performance • Can we use social network information to help us predict popularity?

  6. Overview … • Accurately determining popularity has other applications • Determining advertising rates • Develop marketing strategies • Clearly, inaccurate prediction will result in worst performance • Can we use social network information to help us predict popularity? • Our solution can be applied to applications that have social connectivity information

  7. Our approach … • Try to predict popularity based on social network structure and not on content • E.g. if content viewed by a user with greater connectivity, then will propagate faster than if viewed by user with lower connectivity

  8. Our approach … • Ideal prediction technique • Accurate • Computationally lightweight • Scalable to large scale social media

  9. Our approach … • We incorporate the idea of a “community” into the prediction • A community represents a large group of users who are connected to each other via social graph

  10. Our approach … • Use greedy optimization of modularity to determine community [Clauset et. al., 2004] • Compute by merging nodes into groups that maximizes the modularity score of the graph • Lightweight enough to scale

  11. Our approach … • Try to measure popularity using the connectivity of an individual user within a community • Connection coefficient captures the connectivity of a user within a community • Idea is that higher the connection coefficient, the faster the content will spread to others within the community

  12. Our approach … • Try to measure popularity using the connectivity of a community with respect to other communities • Spreading content within one community may not necessary mean it will spread to the rest of the network • E.g. content may only appeal to a very small niche • So we consider connectivity of community

  13. Our approach … • Try to measure popularity using the connectivity of a community with respect to other communities • Spreading content within one community may not necessary mean it will spread to the rest of the network • E.g. content may only appeal to a very small niche • So we consider connectivity of community

  14. The data … • Evaluated using real world dataset collected from Digg and MetaFilter • Digg dataset was collected by us. Had 5,000 stories from 2,684 authors. A total of 117,956 users, 1,164,613 edges, and 19,645 posts. • Metafilter dataset obtain from database

  15. The data … • In Digg, users can view • Recently promoted stories (front page) • Recently submitted stories • Stories their friends recently submitted • Stories their friends recently voted for • Then they vote for stories that interest them • Popular stories then become “top stories”. Actual algorithm is unknown

  16. Some findings … • Some top stories come from users with many friends • But considerable top stories comes from users with few friends • So fewer friends does not mean stories are not popular

  17. Some findings … • No strict linear correlation between number of votes in first hour, and final number of votes • So something that is initially popular may not mean long term popularity

  18. Some findings … • No “return-the-favor” behavior observed • Users that voted favorably for stories do not necessary get more favorable votes in return

  19. Some findings … • Almost all top stories spread to larger communities very quickly after they first appear • Story popularity depends less on characteristics of author and number of initial support • More relevant factor is whether user is author is member of large or small community • Also relevant is how many users from different communities offer initial support

  20. Thank you

More Related