Abstract
Graphs find a variety of use in numerous domains especially because of their capability to model common problems. The social networking graphs that are used for social networking analysis, a feature given by various social networking sites are an example of this. Graphs can also be visualized in the search engines to carry search operations and provide results. Various searching algorithms have been developed for searching in graphs. In this paper we propose that the entire network graph be clustered. The larger graphs are clustered to make smaller graphs. These smaller graphs can again be clustered to further reduce the size of graph. The search is performed on the smallest graph to identify the general path, which may be further build up to actual nodes by working on the individual clusters involved. Since many searches are carried out on the same graph, clustering may be done once and the data may be used for multiple searches over the time. If the graph changes considerably, only then we may re-cluster the graph.
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References
Karl-Heinrich, A.: A Hierarchical Graph-Clustering Approach to find Groups of Objects. In: ICA Commission on Map Generalization, 5th Workshop on Progress in Automated Map Generalization (2003)
Arcaute, E., Chen, N., Kumar, R., Liben-Nowell, D., Mahdian, M., Nazerzadeh, H., Xu, Y.: Deterministic Decentralized Search in Random Graphs. In: Bonato, A., Chung, F.R.K. (eds.) WAW 2007. LNCS, vol. 4863, pp. 187–194. Springer, Heidelberg (2007)
Ulrik, B., Marco, G., Dorothea, W.: Experiments on graph clustering algorithms. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 568–579. Springer, Heidelberg (2003)
Nick, C., Martin, S.: Random Walks on the Click Graph. iN: SIGIR Conf Research and Development in Information Retrieval 239 (2007)
Goldberg Andrew, V., Chris, H.: Computing the Shortest Path: A* Search Meets Graph Theory. In: Proceedings of SODA, pp. 156–165 (2005)
Simon, G., Horst, B.: Validation indices for graph clustering. Pattern Recognition Letters 24, 1107–1113 (2003)
Hao, H., Haixun, W., Jun, Y., Yu Philip, S.: BLINKS: Ranked Keyword Searches on Graphs. In: The ACM International Conference on Management of Data (SIGMOD), Beijing, China (2007)
Adel, H., Shengrui, W.: A Graph Clustering Algorithm with Applications to Content-Based Image Retrieval. In: Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an (2003)
Varun, K., Shashank, P., Soumen, C., Sudarshan, S., Rushi, D., Hrishikesh, K.: Bidirectional Expansion For Keyword Search on Graph Databases. In: ACM Proceedings of the 31st international conference on Very large data bases Trondheim, Norway (2005)
Marc, N., Wiener Janet, L.: Breadth-First Search Crawling Yields High-Quality Pages. In: ACM Proceedings of the 10th international conference on World Wide Web, Hong Kong (2001)
Rattigan Matthew, J., Marc, M., David, J.: Graph Clustering with Network Structure Indices. In: ACM Proceedings of the 24th international conference on Machine learning Corvalis, Oregon (2007)
Tadikonda Satish, K., Milan, S., Collins Steve, M.: Efficient Coronary Border Detection Using Heuristic Graph Searching. IEEExplore
Yonggu, W., Xiaojuan, L.: Social Network Analysis of Interaction in Online Learning Communities. In: ICALT 2007 Seventh IEEE International Conference on Advanced Learning Technologies (2007)
Jing, Y., Baluja, S.: PageRank for Product Image Search. In: WWW 2008. Refereed Track: Rich Media (2008)
Rong, Z., Hansen Eric, A.: Sparse-Memory Graph Search. In: 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico (2003)
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Kala, R., Shukla, A., Tiwari, R. (2009). Optimized Graph Search Using Multi-Level Graph Clustering. In: Ranka, S., et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_11
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DOI: https://doi.org/10.1007/978-3-642-03547-0_11
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