NDLI: Partitioning Social Networks for Fast Retrieval of Time-Dependent Queries

Content Provider	IEEE Xplore Digital Library
Author	Mindi Yuan Stein, D. Carrasco, B. Trindade, J.M.F. Yi Lu
Copyright Year	2012
Abstract	Online social network (OSN) queries require retrievals of multiple small records generated by different users in the network, and the set of records to be retrieved is time dependent. Current implementation of hash-based partitioning results in accesses at a large number of servers, which significantly degrades response time. Partitioning the OSN friendship graph is difficult as its power-law degree distribution leads to many cross-partition edges. Naive replication requires extra storage that is orders of magnitude larger. In our previous work (2011), we proposed to partition not only the spatial network of social relations, but also in the time dimension so that users who have communicated in a given period are grouped together. We built an activity prediction graph (APG) to keep in one partition newly created data that are highly likely to be accessed together. In this paper, we analyze the distribution of the Facebook wall posts in the New Orleans network. We further emphasize that the objective of partitioning is to keep the two-hop neighborhood of a user in one partition, instead of the one-hop network usually considered. Two-hop neighborhoods are the basic units of retrieval in OSN and can be much larger than one-hop networks. We use a static partitioning method based on KMETIS, and a dynamic local partitioning method that maintains evenness and requires only a small amount of data movement across partitions. For evaluation, the partitioning results are tested with emulation of Facebook page downloads. We show that partitioning on twohop networks yields at lest 19% more local queries than its one-hop counterpart. The static algorithm achieves 5.6 times better data locality than hash-based partitioning and the dynamic algorithm achieves 6.4 times better locality while keeping the number of movements small. Almost all queries are kept in at most 3 partitions for both algorithms.
Starting Page	205
Ending Page	212
File Size	373998
Page Count	8
File Format	PDF
ISBN	9781467316408
e-ISBN	9780769547480
DOI	10.1109/ICDEW.2012.63
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Heuristic algorithms Receivers Prediction algorithms Partitioning algorithms Facebook
Content Type	Text
Resource Type	Article

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