Advances in Knowledge Discovery and Data Mining: 17th by Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian

By Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)

The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed court cases of the seventeenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the full of ninety eight papers offered in those complaints was once rigorously reviewed and chosen from 363 submissions. They hide the final fields of knowledge mining and KDD generally, together with development mining, type, graph mining, functions, laptop studying, function choice and dimensionality aid, a number of details resources mining, social networks, clustering, textual content mining, textual content type, imbalanced info, privacy-preserving facts mining, suggestion, multimedia info mining, circulation information mining, information preprocessing and representation.

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Additional info for Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II

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This section extends the idea to graph datasets and discusses related problems. Assume the input data is a graph G = (V, E), where V = {1, 2, . . , n} is the set of vertices and E = {(i, j)} is the set of edges. Throughout this paper, an undirected graph is treated as its directed version by replacing each edge i ↔ j with two opposite arcs (i, j) and (j, i). We aim to discover a random representative characterized by a discrete distribution α = (α1 , . . , αn ) defined on V, so that any random node in V can communicate with it in the most efficient and economical way.

In: Proc. ICML 1998, pp. 46–54 (1998) 2. : Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. UAI 1998, pp. 43–52 (1998) 3. : User modelling in i-help: What, why, when and how. , Vassileva, J. ) UM 2001. LNCS (LNAI), vol. 2109, pp. 117–126. Springer, Heidelberg (2001) 4. : Collaborative Filtering for People to People Recommendation in Social Networks. In: Li, J. ) AI 2010. LNCS, vol. 6464, pp. 476–485. Springer, Heidelberg (2010) 5. : Learning collaborative filtering and its application to people to people recommendation in social networks.

By doing so, a prior distribution defined over the graph nodes emerges where representatives with high probabilities lie in key positions and are mutually exclusive, reducing neighbour overlap. Evaluation with information diffusion experiments on real scientific collaboration networks shows that seeds selected using SKE are more effective spreaders compared with those selected with traditional ranking algorithms and a state-of-the-art degree discount heuristic. 1 Introduction In a social network, a small subset of representative nodes can help establish a hierarchical messaging scheme: the correspondence with each individual node is through a nearby representative.

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