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Collaborative tagging has emerged as an efficient way to semantically describe online resources shared by a community of users. However, tag descriptions present some drawbacks such as tag scarcity or concept inconsistencies. In these situations, tag recommendation strategies can help users in adding meaningful tags to the resources being described. Freesound is an online audio clip sharing site that uses collaborative tagging to describe a collection of more than 130,000 sound samples. In this paper we propose four algorithm variants for tag recommendation based on tag co-occurrence in the Freesound folksonomy. On the basis of removing a number of tags that have to be later predicted by the algorithms, we find that using ranks instead of raw tag similarities produces statistically significant improvements. Moreover, we show how specific strategies for selecting the appropriate number of tags to be recommended can significantly improve algorithms' performance. These two aspects provide insight into some of the most basic components of tag recommendation systems, and we plan to exploit them in future real-world deployments.

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073_ISMIR_2012.pdf

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Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remain formally unknown. In this contribution we use tools and concepts from statistical physics and complex networks to study and quantify the evolution of western contemporary popular music. In it, we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness. Moreover, we find many of these patterns and metrics to be consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism in this type of music. Nonetheless, we prove important changes or trends. These are related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. The obtained results suggest that our perception of new popular music would be largely rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

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In this paper we benchmark two distinct algorithms for extracting community structure from social networks represented as graphs, considering how we can representatively sample an OSN graph while maintaining its community structure. We also evaluate the extraction algorithms’ optimum value (modularity) for the number of communities using five well-known benchmarking datasets, two of which represent real online OSN data. Also we consider the assignment of the filtering and sampling criteria for each dataset. We find that the extraction algorithms work well for finding the major communities in the original and the sampled datasets. The quality of the results is measured using an NMI (Normalized Mutual Information) type metric to identify the grade of correspondence between the communities generated from the original data and those generated from the sampled data. We find that a representative sampling is possible which preserves the key community structures of an OSN graph, significantly reducing computational cost and also making the resulting graph structure easier to visualize. Finally, comparing the communities generated by each algorithm, we identify the grade of correspondence.

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In this paper we apply different types of clustering,
fuzzy (fuzzy c-Means) and crisp (k-Means) to graph statistical
data in order to evaluate information loss due to perturbation as
part of the anonymization process for a data privacy application.
We make special emphasis on two major node types: hubs, which
are nodes with a high relative degree value, and bridges, which
act as connecting nodes between different regions in the graph.
By clustering the graph's statistical data before and after
perturbation, we can measure the change in characteristics and
therefore the information loss. We partition the nodes into three
groups: hubs/global bridges, local bridges, and all other nodes.
We suspect that the partitions of these nodes are best represented
in the fuzzy form, especially in the case of nodes in frontier
regions of the graphs which may have an ambiguous assignment.

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In this paper we review the state of the art on graph privacy with special emphasis on applications to online social networks, and we consider some novel aspects which have not been greatly covered in the specialized literature on graph privacy. The following key considerations are covered: (i) choice of different operators to modify the graph; (ii) information loss based on the cost of graph operations in terms of statistical characteristics (degree, clustering coefficient and path length) in the original graph; (iii) computational cost of the operations; (iv) in the case of the aggregation of two nodes, the choice of similar adjacent nodes rather than isomorphic topologies, in order to maintain the overall structure of the graph; (v) a statistically knowledgeable attacker who is able to search for regions of a simply anonymized graph based on statistical characteristics and map those onto a given node and its immediate neighborhood.

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