Abstract. This work deals with determination of meaningful and terse cluster labels for News document clusters. We analyze a number of alternatives for selecting headlines and/or sentences of document in a document cluster (obtained as a result of an entity-event-duration query), and formalize an approach to extracting a short phrase from well-supported headlines/sentences of the cluster that can serve as the cluster label. Our technique maps a sentence into a set of significant stems to approximate its semantics, for comparison. Eventually a cluster label is extracted from a selected headline/sentence as a contiguous sequence of words, resuscitating word sequencing information lost in the formalization of semantic equivalence.

The chapter provides a comprehensive and in-depth exploration of two fundamental linear data structures in computer science: Stack and Queue. It begins with an introduction to these data structures, highlighting their key features and fundamental differences. It then explains various operations that can be performed on both data structures, such as push, pop, enqueue, and dequeue. These implementations are thoroughly compared and contrasted, highlighting their advantages and disadvantages. These applications are discussed in detail, showcasing the versatility of Stack and Queue data structures in different scenarios. The chapter includes Python code snippets demonstrating Stack and Queue data structures further to aid the reader's understanding of the material. This chapter provides a comprehensive and practical guide to Stack and Queue data structures, making it a valuable resource for computer science students, researchers, and practitioners.

No abstract

Studying the temporal behaviour of communities is necessary for a deep understanding of communities in social media(SM). In this paper we focus on the use of temporal data and temporal data mining in social networks.