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This work is pertaining to the diversified ranking of web-resources and interconnected documents that rely on a network-like structure, e.g. web-pages. A practical example of this would be a query for the k most relevant web-pages that are also in the same time as dissimilar with each other as possible. Relevance and dissimilarity are quantified using an aggregation of network distance and context similarity. For example, for a specific configuration of the problem, we might be interested in web-pages that are similar with the query in terms of their textual description but distant from each other in terms of the web-graph, e.g. many clicks away. In retrospect, a dearth of work can be found in the literature addressing this problem taking the network structure formed by the document links into consideration. In this work, we propose a hill-climbing approach that is seeded with a document collection which is generated using greedy heuristics to diversify initially. More importantly, we tackle the problem in the context of web-pages where there is an underlying network structure connecting the available documents and resources. This is a significant difference to the majority of works that tackle the problem in terms of either content definitions, or the graph structure of the data, but never addressing both aspects simultaneously. To the best of our knowledge, this is the very first effort that can be found to combine both aspects of this important problem in an elegant fashion by also allowing a great degree of flexibility on how to configure the trade-offs of (i) document relevance over result-items' dissimilarity, and (ii) network distance over content relevance or dissimilarity. Last but not least, we present an extensive evaluation of our methods that demonstrate the effectiveness and efficiency thereof., Comment: 12 pages of unpublished work

In this paper we consider a set of travelers, starting from likely different locations towards a common destination within a road network, and propose solutions to find the optimal connecting points for them. A connecting point is a vertex of the network where a subset of the travelers meet and continue traveling together towards the next connecting point or the destination. The notion of optimality is with regard to a given aggregated travel cost, e.g., travel distance or shared fuel cost. This problem by itself is new and we make it even more interesting (and complex) by considering affinity factors among the users, i.e., how much a user likes to travel together with another one. This plays a fundamental role in determining where the connecting points are and how subsets of travelers are formed. We propose three methods for addressing this problem, one that relies on a fast and greedy approach that finds a sub-optimal solution, and two others that yield globally optimal solution. We evaluate all proposed approaches through experiments, where collections of real datasets are used to assess the trade-offs, behavior and characteristics of each method., Comment: This submission has been withdrawn by arXiv administrators due to disputed authorship