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3,512 results on '"link prediction"'

3. Link prediction for interdisciplinary collaboration via co-authorship network

Author

Yi Yu and Haeran Cho

Subjects
0301 basic medicine, FOS: Computer and information sciences, Physics - Physics and Society, Interdisciplinary collaboration, Computer science, FOS: Physical sciences, Link prediction, 02 engineering and technology, Physics and Society (physics.soc-ph), Statistics - Applications, Co-authorship network, Set (abstract data type), 03 medical and health sciences, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Co authorship, Digital Libraries (cs.DL), Applications (stat.AP), Link (knot theory), Research data, Communication, Computer Science - Digital Libraries, Bipartite network, Data science, Computer Science Applications, Human-Computer Interaction, 030104 developmental biology, Bipartite graph, Information Systems
Abstract

We analyse the Publication and Research (PURE) data set of University of Bristol collected between $2008$ and $2013$. Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network.

Published
2018
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5. iSurfer: A Focused Web Crawler Based on Incremental Learning from Positive Samples

Author

Ye, Yunming, Ma, Fanyuan, Lu, Yiming, Chiu, Matthew, Huang, Joshua, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Yu, Jeffrey Xu, editor, Lin, Xuemin, editor, Lu, Hongjun, editor, and Zhang, Yanchun, editor

Published
2004
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6. Using Markov Models to Define Proactive Action Plans for Users at Multi-viewpoint Websites

Author

Menasalvas, Ernestina, Millán, Socorro, Gonzalez, P., Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Tsumoto, Shusaku, editor, Słowiński, Roman, editor, Komorowski, Jan, editor, and Grzymała-Busse, Jerzy W., editor

Published
2004
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7. Tutorial on Statistical Relational Learning

Author

Getoor, Lise, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Kramer, Stefan, editor, and Pfahringer, Bernhard, editor

Published
2005
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8. Generating Dynamic Higher-Order Markov Models in Web Usage Mining

Author

Borges, José, Levene, Mark, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Jorge, Alípio Mário, editor, Torgo, Luís, editor, Brazdil, Pavel, editor, Camacho, Rui, editor, and Gama, João, editor

Published
2005
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9. Task-Oriented Web User Modeling for Recommendation

Author

Jin, Xin, Zhou, Yanzan, Mobasher, Bamshad, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Ardissono, Liliana, editor, Brna, Paul, editor, and Mitrovic, Antonija, editor

Published
2005
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10. A Hybrid Approach for Spatial Web Personalization

Author

Yang, Yanwu, Claramunt, Christophe, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Li, Ki-Joune, editor, and Vangenot, Christelle, editor

Published
2005
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11. Context-Based Navigational Support in Hypermedia

Author

Stober, Sebastian, Nürnberger, Andreas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wade, Vincent P., editor, Ashman, Helen, editor, and Smyth, Barry, editor

Published
2006
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13. Mining Temporally Changing Web Usage Graphs

Author

Desikan, Prasanna, Srivastava, Jaideep, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Mobasher, Bamshad, editor, Nasraoui, Olfa, editor, Liu, Bing, editor, and Masand, Brij, editor

Published
2006
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14. Automated Social Network Analysis for Collaborative Work

Author

Korba, Larry, Song, Ronggong, Yee, George, Patrick, Andrew, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Luo, Yuhua, editor

Published
2006
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15. Link Discovery in Graphs Derived from Biological Databases : (Research Paper)

Author

Sevon, Petteri, Eronen, Lauri, Hintsanen, Petteri, Kulovesi, Kimmo, Toivonen, Hannu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Leser, Ulf, editor, Naumann, Felix, editor, and Eckman, Barbara, editor

Published
2006
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16. Clustering Scientific Literature Using Sparse Citation Graph Analysis

Author

Bolelli, Levent, Ertekin, Seyda, Giles, C. Lee, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Fürnkranz, Johannes, editor, Scheffer, Tobias, editor, and Spiliopoulou, Myra, editor

Published
2006
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17. DAWN – A System for Context-Based Link Recommendation in Web Navigation

Author

Stober, Sebastian, Nürnberger, Andreas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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18. A Constrained Spreading Activation Approach to Collaborative Filtering

Author

Griffith, Josephine, O’Riordan, Colm, Sorensen, Humphrey, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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19. Cost-Benefit Analysis of Web Prefetching Algorithms from the User’s Point of View

Author

Domènech, Josep, Pont, Ana, Sahuquillo, Julio, Gil, José A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Boavida, Fernando, editor, Plagemann, Thomas, editor, Stiller, Burkhard, editor, Westphal, Cedric, editor, and Monteiro, Edmundo, editor

Published
2006
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21. Building an Effective Representation for Dynamic Networks.

Author

Hill, Shawndra, Agarwal, Deepak K., Bell, Robert, and Volinsky, Chris

Subjects
*GRAPH theory, *INFORMATION theory, *COMPUTER networks, *TELECOMMUNICATION, *DATA transmission systems, *INTERNET fraud, *APPROXIMATION theory, *MATHEMATICAL statistics
Abstract

A dynamic network is a special type of network composed of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to efficient large-scale analysis is a challenge. In this article, we represent dynamic graphs using a data structure introduced in an earlier article. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present a preliminary analysis on Web logs and e-mail networks. [ABSTRACT FROM AUTHOR]

Published
2006
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22. Manipulation-Resistant Reputations Using Hitting Time

Author

Hopcroft, John, Sheldon, Daniel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bonato, Anthony, editor, and Chung, Fan R. K., editor

Published
2007
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23. Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis

Author

Airoldi, Edoardo M., Blei, David M., Fienberg, Stephen E., Xing, Eric P., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Airoldi, Edoardo, editor, Blei, David M., editor, Fienberg, Stephen E., editor, Goldenberg, Anna, editor, Xing, Eric P., editor, and Zheng, Alice X., editor

Published
2007
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24. Learning Approximate MRFs from Large Transactional Data

Author

Wang, Chao, Parthasarathy, Srinivasan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Airoldi, Edoardo, editor, Blei, David M., editor, Fienberg, Stephen E., editor, Goldenberg, Anna, editor, Xing, Eric P., editor, and Zheng, Alice X., editor

Published
2007
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25. Estimating Number of Citations Using Author Reputation

Author

Castillo, Carlos, Donato, Debora, Gionis, Aristides, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ziviani, Nivio, editor, and Baeza-Yates, Ricardo, editor

Published
2007
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26. Website link prediction using a Markov chain model based on multiple time periods.

Author

Jayalal, Shantha, Hawksley, Chris, and Brereton, Pearl

Subjects
WEBSITES, WEB servers, INTERNET searching, MARKOV processes, COMPUTER network resources
Abstract

Growing size and complexity of many websites have made navigation through these sites increasingly difficult. Attempting to automatically predict the next page for a website user to visit has many potential benefits, for example in site navigation, automatic tour generation, adaptive web applications, recommendation systems, web server optimisation, web search and web pre-fetching. This paper describes an approach to link prediction using a Markov chain model based on an exponentially smoothed transition probability matrix which incorporates site usage statistics collected over multiple time periods. The improved performance of this approach compared to earlier methods is also discussed. [ABSTRACT FROM AUTHOR]

Published
2007
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27. Predicting unobserved links in incompletely observed networks

Author

Marchette, David J. and Priebe, Carey E.

Subjects
*GRAPH theory, *SOCIAL groups, *RANDOM graphs, *SOCIAL networks
Abstract

Abstract: In this paper we consider networks in which the links (edges) are imperfectly observed. This may be a result of sampling, or it may be caused by actors (vertices) who are actively attempting to hide their links (edges). Thus the network is incompletely observed, and we wish to predict which of the possible unobserved links are actually present in the network. To this end, we apply a constrained random dot product graph (CRDPG) to rank the potential edges according to the probability (under the model) that they are in fact present. This model is then extended to utilize covariates measured on the actors, to improve the link prediction. The method is illustrated on a data set of alliances between nations, in which a subset of the links (alliances) is assumed unobserved for the purposes of illustration. [Copyright &y& Elsevier]

Published
2008
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28. Telephone Call Network Data Mining: A Survey with Experiments

Author

Kurucz, Miklós, Lukács, László, Silklói, Dávid, Benczúr, András A., Csalogány, Károly, Lukács, András, Tóth, Gábor Fejes, editor, Katona, Gyula O. H., editor, Lovász, László, editor, Pálfy, Péter Pál, editor, Recski, András, editor, Stipsicz, András, editor, Szász, Domokos, editor, Miklós, Dezső, editor, Bollobás, Béla, editor, and Kozma, Robert, editor

Published
2008
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29. Semantic and Event-Based Approach for Link Prediction

Author

Wohlfarth, Till, Ichise, Ryutaro, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Yamaguchi, Takahira, editor

Published
2008
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30. Just Add Weights: Markov Logic for the Semantic Web

Author

Domingos, Pedro, Lowd, Daniel, Kok, Stanley, Poon, Hoifung, Richardson, Matthew, Singla, Parag, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, da Costa, Paulo Cesar G., editor, d’Amato, Claudia, editor, Fanizzi, Nicola, editor, Laskey, Kathryn B., editor, Laskey, Kenneth J., editor, Lukasiewicz, Thomas, editor, Nickles, Matthias, editor, and Pool, Michael, editor

Published
2008
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31. Exploiting non-redundant local patterns and probabilistic models for analyzing structured and semi-structured data

Author

Wang, Chao

Subjects
Computer Science, Probabilistic Graphical Model, Undirected Graphical Model, Markov Random Field, Maximum Entropy, Feature Selection, Frequent Pattern Mining, Non-Redundant Frequent Pattern, Structured and Semi-Structure Data, Social Network Analysis, Link Prediction
Abstract

This work seeks to develop a probabilistic framework for modeling, querying and analyzing large-scale structured and semi-structured data. The framework has three components: (1) Mining non-redundant local patterns from data; (2) Gluing these local patterns together by employing probabilistic models (e.g., Markov random field (MRF), Bayesian network); and (3) Reasoning over the data for solving various data analysis tasks. Our contributions are as follows: (a) We present an approach of employing probabilistic models to identify non-redundant itemset patterns from a large collection of frequent itemsets on transactional data. Our approach can effectively eliminate redundancies from a large collection of itemset patterns. (b) We propose a technique of employing local probabilistic models to glue non-redundant itemset patterns together in tackling the link prediction task in co-authorship network analysis. Our technique effectively combines topology analysis on network structure data and frequency analysis on network event log data. The main idea is to consider the co-occurrence probability of two end nodes associated with a candidate link. We propose a method of building MRFs over local data regions to compute this co-occurrence probability. Experimental results demonstrate that the co-occurrence probability inferred from the local probabilistic models is very useful for link prediction. (c) We explore employing global models, models over large data regions, to glue non-redundant itemset patterns together. We investigate learning approximate global MRFs on large transactional data and propose a divide-and-conquer style modeling approach. Empirical study shows that the models are effective in modeling the data and approximately answering queries on the data. (d) We propose a technique of identifying non-redundant tree patterns from a large collection of structural tree patterns on semi-structured XML data. Our approach can effectively eliminate redundancies from a large collection of structural tree patterns. Furthermore, we present techniques of employing these non-redundant tree patterns as summary statistics for the XML data to solve the XML twig selection estimation problem. We propose a probabilistic framework under which the selectivity of a twig query can be estimated from the information of its subtrees. Empirical results demonstrate the efficacy of our approach on real and synthetic datasets.

Published
2008

34. On Pairwise Kernels: An Efficient Alternative and Generalization Analysis.

Author

Kashima, Hisashi, Oyama, Satoshi, Yamanishi, Yoshihiro, and Tsuda, Koji

Abstract

Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and become successful in various fields. In this paper, we propose an efficient alternative which we call Cartesian kernel. While the existing pairwise kernel (which we refer to as Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph which is more sparse than the Kronecker product graph. Experimental results show the Cartesian kernel is much faster than the existing pairwise kernel, and at the same time, competitive with the existing pairwise kernel in predictive performance.We discuss the generalization bounds by the two pairwise kernels by using eigenvalue analysis of the kernel matrices. [ABSTRACT FROM AUTHOR]

Published
2009
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35. On Link Privacy in Randomizing Social Networks

Author

Ying, Xiaowei, Wu, Xintao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Theeramunkong, Thanaruk, editor, Kijsirikul, Boonserm, editor, Cercone, Nick, editor, and Ho, Tu-Bao, editor

Published
2009
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36. Enhancing a navigational based recommender system by link prediction methods

Author

Esslimani, Ilham, Brun, Armelle, Boyer, Anne, Knowledge Information and Web Intelligence (KIWI), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
usage analysis, collaborative filtering, behavioral network, social network, recommender systems, link prediction, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

Recommender systems are widely used for personalization of information on the web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommen- dation technique. However, Classical CF (CCF) systems use only direct links and common features to model relationships between users. This report presents a Behavioral Network Collaborative Filtering approach (BNCF) that uses navi- gational patterns to model relationships between users and exploits social networks techniques, such as prediction link methods, to explore additional links throughout the behav- ioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. The proposed approach is evaluated in terms of ac- curacy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions in terms of HMAE. Besides, the evaluation of a combined model (that exploits both best accurate BNCF and CCF based models) shows the importance of combining similari- ties of two different networks and its impact on accuracy of high predictions.

Published
2009

39. Describing User Interactions in Adaptive Interactive Systems

Author

Bezold, Matthias, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Houben, Geert-Jan, editor, McCalla, Gord, editor, Pianesi, Fabio, editor, and Zancanaro, Massimo, editor

Published
2009
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40. Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection

Author

Arnold, Andrew, Cohen, William W., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Liu, Benyuan, editor, Bestavros, Azer, editor, Du, Ding-Zhu, editor, and Wang, Jie, editor

Published
2009
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41. The Time-Series Link Prediction Problem with Applications in Communication Surveillance.

Author

Zan Huang and Dennis K. J. Lin

Subjects
DATA mining, TIME series analysis, PREDICTION models, ONLINE social networks, COMPUTER networks, PROBABILITY theory, ALGORITHMS, GRAPH algorithms
Abstract

The ability to predict linkages among data objects is central to many data mining tasks, such as product recommendation and social network analysis. Substantial literature has been devoted to the link prediction problem either as an implicitly embedded problem in specific applications or as a generic data mining task. This literature has mostly adopted a static graph representation where a snapshot of the network is analyzed to predict hidden or future links. However, this representation is only appropriate to investigate whether a certain link will ever occur and does not apply to many applications for which the prediction of the repeated link occurrences are of primary interest (e.g., communication network surveillance). In this paper, we introduce the time-series link prediction problem, taking into consideration temporal evolutions of link occurrences to predict link occurrence probabilities at a particular time. Using Enron e-mail data and high-energy particle physics literature coauthorship data, we have demonstrated that time-series models of single-link occurrences achieve comparable link prediction performance with commonly used static graph link prediction algorithms. Furthermore, a combination of static graph link prediction algorithms and time-series models produced significantly better predictions over static graph link prediction methods, demonstrating the great potential of integrated methods that exploit both interlink structural dependencies and intralink temporal dependencies. [ABSTRACT FROM AUTHOR]

Published
2009
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42. A Survey on Statistical Relational Learning

Author

Khosravi, Hassan, Bina, Bahareh, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Farzindar, Atefeh, editor, and Kešelj, Vlado, editor

Published
2010
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43. Using Friendship Ties and Family Circles for Link Prediction

Author

Zheleva, Elena, Getoor, Lise, Golbeck, Jennifer, Kuter, Ugur, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Giles, Lee, editor, Smith, Marc, editor, Yen, John, editor, and Zhang, Haizheng, editor

Published
2010
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44. Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large Networks

Author

Esfandiar, Pooya, Bonchi, Francesco, Gleich, David F., Greif, Chen, Lakshmanan, Laks V. S., On, Byung-Won, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Kumar, Ravi, editor, and Sivakumar, Dandapani, editor

Published
2010
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45. Cartesian Kernel : An Efficient Alternative to the Pairwise Kernel

Abstract

Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and has been used successfully in several fields. In this paper, we propose an efficient alternative which we call a Cartesian kernel. While the existing pairwise kernel (which we refer to as the Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph, which is more sparse than the Kronecker product graph. We discuss the generalization bounds of the two pairwise kernels by using eigenvalue analysis of the kernel matrices. Also, we consider the N-wise extensions of the two pairwise kernels. Experimental results show the Cartesian kernel is much faster than the Kronecker kernel, and at the same time, competitive with the Kronecker kernel in predictive performance.

Published
2010

46. Cartesian Kernel : An Efficient Alternative to the Pairwise Kernel

Abstract

Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and has been used successfully in several fields. In this paper, we propose an efficient alternative which we call a Cartesian kernel. While the existing pairwise kernel (which we refer to as the Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph, which is more sparse than the Kronecker product graph. We discuss the generalization bounds of the two pairwise kernels by using eigenvalue analysis of the kernel matrices. Also, we consider the N-wise extensions of the two pairwise kernels. Experimental results show the Cartesian kernel is much faster than the Kronecker kernel, and at the same time, competitive with the Kronecker kernel in predictive performance.

Published
2010

47. Cartesian Kernel : An Efficient Alternative to the Pairwise Kernel

Abstract

Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and has been used successfully in several fields. In this paper, we propose an efficient alternative which we call a Cartesian kernel. While the existing pairwise kernel (which we refer to as the Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph, which is more sparse than the Kronecker product graph. We discuss the generalization bounds of the two pairwise kernels by using eigenvalue analysis of the kernel matrices. Also, we consider the N-wise extensions of the two pairwise kernels. Experimental results show the Cartesian kernel is much faster than the Kronecker kernel, and at the same time, competitive with the Kronecker kernel in predictive performance.

Published
2010

48. The Link Prediction Problem in Bipartite Networks

Author

Kunegis, Jérôme, De Luca, Ernesto W., Albayrak, Sahin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Hüllermeier, Eyke, editor, Kruse, Rudolf, editor, and Hoffmann, Frank, editor

Published
2010
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49. Predicting labels for dyadic data

Author

Menon, Aditya Krishna and Menon, Aditya Krishna

Abstract

In dyadic prediction, the input consists of a pair of items (a dyad), and the goal is to predict the value of an observation related to the dyad. Special cases of dyadic prediction include collaborative filtering, where the goal is to predict ratings associated with (user, movie) pairs, and link prediction, where the goal is to predict the presence or absence of an edge between two nodes in a graph. In this paper, we study the problem of predicting labels associated with dyad members. Special cases of this problem include predicting characteristics of users in a collaborative filtering scenario, and predicting the label of a node in a graph, which is a task sometimes called within-network classification or relational learning. This paper shows how to extend a recent dyadic prediction method to predict labels for nodes and labels for edges simultaneously. The new method learns latent features within a log-linear model in a supervised way, to maximize predictive accuracy for both dyad observations and item labels. We compare the new approach to existing methods for within-network classification, both experimentally and analytically. The experiments show, surprisingly, that learning latent features in an unsupervised way is superior for some applications to learning them in a supervised way.

Published
2010

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