@article {102,
	title = {Pre-processing and Path Normalization of a web graph used as a social network},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2007},
	pages = {262 - 276},
	abstract = {Many efforts have been made for compression of web graphs. Most of these methods are suitable for search engines and are centered around encoding links and URLs efficiently. The purpose is to handle a large set of web pages in the main memory against any web based search. The authors of the present paper are interested in studying web graph as a social network and to develop a data model for it. So, suitable compression techniques, which are space efficient for disk based storage, are required. This paper has provided a two level compression technique. In the first level, the structural properties of a graph are studied and strongly connected components are fused to reduce the original graph to a DAG. Paths on this DAG are then stored efficiently using a Path Normalization technique. Space complexity expressions indicate the efficiency of the method. Relevant operators required for accessing the original graph through the compressed representations have also been discussed. Important earlier works have been referred to indicate the requirements of the present approach.},
	keywords = {Compression technique, Path normailisation technique, Social software, Web search},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-57949092373\&partnerID=40\&md5=08a3428b63ad14c630820944a0e9c222},
	author = {Teja, C.B. and Mitra, S. and Bagchi, A. and Bandyopadhyay, A.K.}
}
@article {100,
	title = {Social search with missing data: Which ranking algorithm?},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {249 - 261},
	abstract = {Online social networking tools are extremely popular, but can miss potential discoveries latent in the social {\textquoteright}fabric{\textquoteright}. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user{\textquoteright}s search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on {\textquoteright}inferred profiles{\textquoteright} of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods.},
	keywords = {Instant messaging, Ranking algorithms, Relation discovery, Social software},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350629888\&partnerID=40\&md5=a263e560496728c004285a55b37a4e27},
	author = {Zhu, J. and Eisenstadt, M. and Gon{\c c}alves, A. and Denham, C. and Uren, V. and Song, D.}
}
