01809nas a2200217   4500008004100000245008200041210006900123260000900192300001400201490000600215520109000221653002601311653003401337653002001371653001501391100001501406700001401421700001501435700002401450856011701474       2007                        eng d00aPre-processing and Path Normalization of a web graph used as a social network0 aPreprocessing and Path Normalization of a web graph used as a so  c2007  a262 - 2760 v53 aMany 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.10aCompression technique10aPath normailisation technique10aSocial software10aWeb search1 aTeja, C.B.1 aMitra, S.1 aBagchi, A.1 aBandyopadhyay, A.K.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-57949092373&partnerID=40&md5=08a3428b63ad14c630820944a0e9c22201977nas a2200241   4500008004100000245006200041210006000103260000900163300001400172490000600186520124700192653002201439653002301461653002301484653002001507100001201527700001901539700001901558700001501577700001301592700001301605856011701618       2007                        eng d00aSocial search with missing data: Which ranking algorithm?0 aSocial search with missing data Which ranking algorithm  c2007  a249 - 2610 v53 aOnline social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. 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'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 'inferred profiles' 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.10aInstant messaging10aRanking algorithms10aRelation discovery10aSocial software1 aZhu, J.1 aEisenstadt, M.1 aGonçalves, A.1 aDenham, C.1 aUren, V.1 aSong, D.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350629888&partnerID=40&md5=a263e560496728c004285a55b37a4e27