Emoke-Ágnes Horvát

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Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to(More)
Traditional measures of success for film, such as box-office revenue and critical acclaim, lack the ability to quantify long-lasting impact and depend on factors that are largely external to the craft itself. With the growing number of films that are being created and large-scale data becoming available through crowd-sourced online platforms, an endogenous(More)
A bipartite structure is a common property of many real-world network data sets such as agents which are affiliated with societies, customers who buy, rent, or rate products, and authors who write scientific papers. The one-mode projection of these networks onto either set of entities (e.g., societies, products, and articles) is a well-established approach(More)
Several important social network data sets have an inherent bipartite structure: for example, agents are affiliated with societies, authors write articles, customers buy, rent, or rate products. One commonly used network analytic approach to their analysis involves projecting them, i.e., deducing relations between actors of the same type (e.g. societies,(More)
When a structural characteristic of a network is measured, the observed value needs to be compared to its expected value in a random graph model to assess the statistical significance of its occurrence. The random graph model with which the observed graph is compared is chosen to be structurally similar to the real-world network in some aspects and totally(More)
SUMMARY Interactions between various types of molecules that regulate crucial cellular processes are extensively investigated by high-throughput experiments and require dedicated computational methods for the analysis of the resulting data. In many cases, these data can be represented as a bipartite graph because it describes interactions between elements(More)
Many large network data sets are noisy and contain links representing low-intensity relationships that are difficult to differentiate from random interactions. This is especially relevant for high-throughput data from systems biology, large-scale ecological data, but also for Web 2.0 data on human interactions. In these networks with missing and spurious(More)
Crowds offer a new form of efficacious collective decision making, yet knowledge about the mechanisms by which they achieve superior outcomes remains nascent. It has been suggested that crowds work best with market-like relationships when individuals make independent decisions and possess dissimilar information. By contrast, sociological discussions of(More)
Could online social networks like Facebook be used to infer relationships between non-members? We show that the combination of relationships between members and their e-mail contacts to non-members provides enough information to deduce a substantial proportion of the relationships between non-members. Using structural features we are able to predict(More)