The utility of a dense subgraph in gaining a better understanding of a graph has been formalised in numerous ways, each striking a different balance between approximating actual interestingness and computational efficiency. A difficulty in making this trade-off is that, while computational cost of an algorithm is relatively well-defined, a pattern's interestingness is fundamentally subjective. This means that this latter aspect is often treated only informally or neglected, and instead some form of density is used as a proxy. We resolve this difficulty by formalising what makes a dense subgraph pattern interesting to a given user. Unsurprisingly, the resulting measure is dependent on the prior beliefs of the user about the graph. For concreteness, in this paper we consider two cases: one case where the user only has a belief about the overall density of the graph, and another case where the user has prior beliefs about the degrees of the vertices. Furthermore, we illustrate how the resulting interestingness measure is different from previous proposals. We also propose effective exact and approximate algorithms for mining the most interesting dense subgraph according to the proposed measure. Usefully, the proposed interestingness measure and approach lend themselves well to iterative dense subgraph discovery. Contrary to most existing approaches, Learn (2016) 105:41-75 our method naturally allows subsequently found patterns to be overlapping. The empirical evaluation highlights the properties of the new interestingness measure given different prior belief sets, and our approach's ability to find interesting subgraphs that other methods are unable to find.
The amount of information available on the world wide web keeps growing at an exponential pace. Social tagging is a feature of various online social networks to organize information elements by letting people label these with free-form text, called tags. The graph created by this process is often called a folksonomy and comprises the association between people, tags and documents. Tagging is now used to organize web pages, pictures, videos, music, books, academic publications, etc.The current ways of navigating folksonomies are limited. In most web portals, "search" is the main feature which uses tags. When browsing tags, most systems give a few related tags to the clicked tag, none enables the user to get related tags to multiple clicked tags at the same time. A popular tag cloud displays links to the most popular tags in the folksonomy with a font size that depends on their popularity. Popular tag clouds and related tags can enable tag-based navigation.Enabling navigation through related tag clouds to multiple clicked tags in an efficient and scalable manner is a hard problem. We propose a bayesian approach to the problem of generating related tag clouds for navigation by using social network information and probabilistic models of people's tagging behaviors. We propose two new models to generate tag clouds based on popularity, tag cooccurrence and social relationships. The models are implemented in a prototype application to navigate empirical data from "last.fm", an online social network for music. We give an evaluation plan to compare the models regarding searchability through user evaluations.
This chapter introduces the general vision of the Social Semantic Desktop (SSD) and details it in the context of the NEPOMUK project. It outlines the typical SSD requirements and functionalities that were identified from real world scenarios. In addition, it provides the design of the standard SSD architecture together with the ontology pyramid developed to support it. Finally, the chapter gives an overview of some of the technical challenges that arise from the actual development process of the SSD.
This chapter introduces the general vision of the Social Semantic Desktop (SSD) and details it in the context of the NEPOMUK project. It outlines the typical SSD requirements and functionalities that were identified from real world scenarios. In addition, it provides the design of the standard SSD architecture together with the ontology pyramid developed to support it. Finally, the chapter gives an overview of some of the technical challenges that arise from the actual development process of the SSD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.