X-RIME: Hadoop based large scale social network analysis

http://xrime.sourceforge.net/


Today's Internet-based social network sites possess huge user communities. They hold large amount of data about their users and want to generate core competency from the data. A key enabler for this is a cost efficient solution for social data management and social network analysis (SNA). Such a solution faces a few challenges. The most important one is that the solution should be able to handle massive and heterogeneous data sets. Facing this challenge, the traditional data warehouse based solutions are usually not cost efficient enough. On the other hand, existing SNA tools are mostly used in single workstation mode, and not scalable enough. To this end, low cost and highly scalable data management and processing technologies from cloud computing society should be brought in to help. However, most of existing cloud based data analysis solutions are trying to provide SQL-like general purpose query languages, and do not directly support social network analysis. This makes them hard to optimize and hard to use for SNA users. So, we came up with X-RIME to fix this gap. So, briefly speaking, X-RIME wants to provide a few value-added layers on top of existing cloud infrastructure, to support smart decision loops based on massive data sets and SNA. To end users, X-RIME is a library consists of Map-Reduce programs, which are used to do raw data pre-processing, transformation, SNA metrics and structures calculation, and graph / network visualization. The library could be integrated with other Hadoop based data warehouses (e.g., HIVE) to build more comprehensive solutions.

This project is a join effort of Beijing University of Posts and Telecommunications (BUPT) and IBM China Research Lab, supported by IBM Open Collaboration Research program. The code is open source under Apache License V2.0.
Currently Supported SNA Metrics and Structures

vertex degree statistics
weakly connected components (WCC)
strongly connected components (SCC)
bi-connected components (BCC)
ego-centric network density
bread first search / single source shortest path (BFS/SSSP)
K-core
maximal cliques
pagerank
hyperlink-induced topic search (HITS)
minimal spanning tree (MST)
grid variant of Fruchterman-Reingold network layout algorithm