Related papers: Measuring the Accuracy of Linked Data Indices

Graph centrality measures use the structure of a network to quantify central or "important" nodes, with applications in web search, social media analysis, and graphical data mining generally. Traditional centrality measures such as the well…

Data centers have become ubiquitous for today's businesses. From banks to startups, they rely on cloud infrastructure to deploy user applications. In this context, it is vital to provide users with application performance guarantees.…

There are fundamental differences between citation networks and other classes of graphs. In particular, given that citation networks are directed and acyclic, methods developed primarily for use with undirected social network data may face…

The problem of link prediction has attracted considerable recent attention from various domains such as sociology, anthropology, information science, and computer sciences. A link prediction algorithm is proposed based on link similarity…

Plenty of algorithms for link prediction have been proposed and were applied to various real networks. Among these works, the weights of links are rarely taken into account. In this paper, we use local similarity indices to estimate the…

Often information relevant to a decision is summarized in an index number. This paper explores conditions under which conclusions using index numbers are relevant to the decision that needs to be made. Specifically it explores the idea that…

Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or…

A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely…

A focused crawler traverses the web selecting out relevant pages to a predefined topic and neglecting those out of concern. While surfing the internet it is difficult to deal with irrelevant pages and to predict which links lead to quality…

Link prediction (LP) is an important problem in network science and machine learning research. The state-of-the-art LP methods are usually evaluated in a uniform setup, ignoring several factors associated with the data and application…

Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using…

Finding patterns in graphs is a fundamental problem in databases and data mining. In many applications, graphs are temporal and evolve over time, so we are interested in finding durable patterns, such as triangles and paths, which persist…

The correlation of the result lists provided by search engines is fundamental and it has deep and multidisciplinary ramifications. Here, we present automatic and unsupervised methods to assess whether or not search engines provide results…

Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify future, deleted, and redundant links using…

In complex scale-free networks, ranking the individual nodes based upon their importance has useful applications, such as the identification of hubs for epidemic control, or bottlenecks for controlling traffic congestion. However, in most…

We present a large-scale analysis of the accuracy of citation data in the Web of Science and Scopus databases. The analysis is based on citations given in publications in Elsevier journals. We reveal significant data quality problems for…

One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the Internet explosion and the massive diffusion of…

Distributed intrustion detection systems detect attacks on computer systems by analyzing data aggregated from distributed sources. The distributed nature of the data sources allows patterns in the data to be seen that might not be…

Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high-dimensionality of networks pose unique challenges to traditional dependency…

Nowadays, the Internet is indispensable when it comes to information dissemination. People rely on the Internet to inform themselves on current news events, as well as to verify facts. We, as a community, are quickly approaching an…