Volume-3 , Issue-10 , Oct - 2015 Go Back

The web crawler is the central component of a search engine which works like an indexer, finds out hyperlinks and computes keyword density of each web page. It assigns a page rank to each crawled web page by using some ranking algorithm and stores the visited links for the future use. Search results retrieve very fast from desktop browser, but it takes more time when user is on mobile. When a keyword is searched from a mobile browser, the traditional web servers take a long time to interpret this request. Also, the web server has to format search results into the form which mobile device can interpret. Thus, to eliminate this overhead on the web server, a Mobile Application Server has to be introduced instead of the web server to interpret requests from mobile devices.

Search engine; Crawler; Web server; Mobile application server; Wireless markup language; Wireless application protocol

[1] Feng Zhao, Jingyu Zhou, Chang Nie, Heqing Huang and Hai Jin, “SmartCrawler : A two-stage crawler for efficiently harvesting deep-web interfaces”, Services Computing, IEEE Transactions, Volume-PP, Issue-99, DOI-10.11.09, 2015.
[2] Mehdi Bahrami, Mukesh Singhal and Zixuan Zhuang, “A Cloud-based Web Crawler Architecture”, Intelligence in Next Generation Networks (ICIN), 2015 18th International Conference, Page No (216-233), 17-19 Feb 2015.
[3] Kausar M. A., Nasar M. and Singh S. K., “Maintaining the repository of search engine freshness using mobile crawler”, Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy (AICERA/ICMiCR), 2013 Annual International Conference, Page No (1-6), 2013.
[4] Jian-Hong Liu, Jing Chen, Yi-Li Wu and Pei-Li Wang, “AASMP - Android Application Server for Mobile Platforms”, IEEE 2013 16th International Conference on Computational Science and Engineering, Page No (643-650), 2013.
[5] Vinay Kancherla, “Smart Crawler for a Concept based Semantic Search Engine”, Master's Theses and Graduate Research at SJSU ScholarWorks, Paper 380, 12 Jan 2014.
[6] Pavalam S.M, S.V. KumarRaja, M. Jawhar and Felix K. Akorli, “Web crawler in mobile systems”, International Journal of Machine Learning and Computing, Volume-02, Issue-04, Page No (531-534), August 2012.
[7] Beena Mahar and C K Jha, “A comparative study on Web crawling for searching Hidden web”, International Journal of Computer Science and Information Technologies, Volume-02, Issue-03, Page No (2159-2163), 2015.
[8] Vladislav Shkapenyuk and Torsten Suel, “Design and implementation of a high-performance distributed Web crawler”, Data Engineering, 2002. IEEE Proceedings. 18th International Conference, Page No (357-368), 2002.
[9] Jason Rennie and Andrew McCallum, “Using reinforcement learning to spider the web efficiently”, Proceedings of the 16th International Conference on Machine Learning (ICML), Page No (335-343), 1999.
[10] Hardik P. Trivedi, Gaurav N. Daxini, Jignesh A. Oswal, Vinay D. Gor and Swati Mali, “An Approach to Design Personalized Focused Crawler”, International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (144-147), March 2014.
[11] “Mobile Application Server”,
http://www.mobileinfo.com/application_servers.htm ,
4 July, 2015.
[12] “Search Engine”,
http://websearch.about.com/od/enginesanddirectories/a/searchengine.htm , 15 July, 2015.

Kukreja Kajal, Gavali Nishigandha and Khedlekar Gandhali, "Survey on Mobile Optimized Search Crawler," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.99-102, 2015.

Predicting is future relationships from a given snapshot of a network or to infer the interactions among existing members that are likely to occur in the near future is called as link prediction. One of the interesting areas of research in social network is prediction of links. There are various techniques for inferring missing links or additional links that are not directly visible but may occur in the future. Random walk is a popular approach which uses node and edge features to solve the problem of link prediction. Supervised random walks combine the network structure with the characteristics of nodes and edges and acts as a powerful tool for predicting the missing and future links. In this paper a study has been made on various algorithms that uses supervised random walk approach for predicting links in social networks.

Social networks, Link prediction, Supervised Random walk

[1] Abir De, Niloy Ganguly and Soumen Chakrabarti, Discriminative Link Prediction using Local Links, Node Features and Community Structure, 2013, pp.1009-1018.
[2] L.Backstrom and J. Leskovec, Supervised random walks: Predicting and recommending links in social networks, WSDM '11 Proceedings of the fourth ACM international conference, 2011, pp.635-644.
[3] Ervin Tasnadi and Gabor Berend-Supervised prediction of social network links using implicit sources of information, Proceedings of the 24th international conference,2015, pp.1117-1122.
[4] M. A. Hasan,V. Chaoji, S. Salem, and M. Zaki-Link prediction using supervised learning, SDM 06 workshop on Link Analysis, Counterterrorism and Security, 2006.
[5] D. Liben-Nowell and J. Kleinberg, The link prediction problem for social networks, Proceedings of CIKM, 2003, pp.556–559.
[6] László Lovász- Random Walks on Graphs: A Survey, Paul Erd˝os is Eighty(Volume2)Keszthely (Hungary), 1993,pp.1–46.
[7] R.N.Lichtenwalter,J.T.Lussier, and N.V. Chawla-New perspectives and methods in link prediction, KDD '10 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010,pp.243–252.
[8] Ting Jin, Tong Xu, Enhong Chen, Qi Liu, Haiping Ma, Jingsong Lv, Guoping Hu, Random Walk with Pre-filtering for Social Link Prediction,Proceedings of 2013 9th International conference on computational Intelligence and security,2013, pp.139 – 143.
[9] ValdisKrebs,Mapping networks of terrorist cells, Connections,Winter 2002, pp.24(3):43-52.
[10] C.Wang, V.Satuluri, and S.Parthasarathy,Local probabilistic models for link prediction, Proceedings of the 2007 7th IEEE ICDM,IEEE Computer Society, 2007, pp. 322-331.
[11] Zhijun yin,Manish Gupta, Tim Weninger and Jiawei Han, A Unified Framework for Link Recommendation Using Random Walks, Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference, 2010,pp.152 – 159.

A.Vihashini and G.T.Prabavathi, "Supervised Random Walks for Predicting Links in Social Networks: A Study," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.103-105, 2015.