Social network analysis, as an interdisciplinary research field, has drawn an increasing attention in
decade. Inspired by various interactions among inviduals, we developed this Social Network Computation
Paltform (short for SNCP), which is capable of conducting algorithms on a variety of real-world datasets
and providing vivid visualization tool for researchers.
We have developed some pages for exhibiting community detection, link prediction, SIR model, etc. We also
provides a data formatting tool, aimming to converting the different datasets into an desired format.
Thanks for your participation in this platform. The following figure shows the relationships of registered
users, where the link connected with A and B represent user A follows B or User B follows A. Put your mouse
over the node to check its neighbors. The size of a focal node is determined by the number of its neighbors,
i.e., the more neighbors, the larger node. The text alongside an link is the time of this followship
Community structure (or Cluster), as a densely connected group of nodes, is widely existed in many complex
systems, such as social networks, biological networks and so on.
Numerous of algorithms have been proposed to solve the problem of detecting communities in complex networks,
e.g, GN , which employs cutting maximum edge-betweenness strategy and has a good performance under the
evaluation of Modularity(Q) . The comparison of classic algorithms is represented here. Some of the datasets are
from Mark Newman's blog .
Link Prediction  mainly includes the prediction of unknown links and future links.
Unknown links prediction
aims to predict the missing data based on small parts of a large-scale network to reduce the experimental
recover data when the network is initially incomplete. Future links is to estimate the links that will be
appear in the future.
The link prediction page is here.
 M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,” Proceedings of
the National Academy of Sciences, vol. 99, no. 12, pp. 7821–7826, Jun. 2002.
 M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Phys Rev E Stat
Nonlin Soft Matter Phys, vol. 69, no. 2, p. 026113, 2004.
 Getoor, Lise, and Christopher P. Diehl. "Link mining: a survey." ACM SIGKDD Explorations Newsletter 7.2