Katarzyna Musial-Gabrys was invited to present her work on complex social networks during the upcoming workshop organised by the Alan Turing Institute within the Foundation of Social Data Science initiative.
The Alan Turing Institute was established in 2015 as the UK national institute for the data sciences in response to a letter from the Council for Science and Technology (CST) to the UK Prime Minister (7 June 2013), describing the “Age of Algorithms”. The letter presents a case that “The Government, working with the universities and industry, should create a National Centre to promote advanced research and translational work in algorithms and the application of data science.” (https://www.gov.uk/government/publications/the-age-of-algorithms).
Katarzyna’s presentation will contribute to shaping the portfolio of research challenges to be addressed within the Alan Turing Institute.
Title of Katarzyna’s talk: Methodological challenges in data aggregation in complex social networks.
Abstract of the talk:
For the first time in history, we have the possibility to process ‘big data’ (gathered in computer systems) about the interactions and activities of millions of individuals. It represents an increasingly important yet underutilized resource because due to the scale, complexity and dynamics, social networks extracted from this data are extremely difficult to analyse. There is no coherent and comprehensive methodological approach to analyse such networks which is crucial to advance our understanding of continuously changing people’s behaviour.
One of the methodological challenges is to cope with the variety of available big social data. This data comes from multiple systems (email, instant messengers, blogs, social networking sites, google searches, YouTube, etc.); in each system user can have one or more accounts; this data describes different types of activities (commenting, sharing, messaging, calling, etc.) and relationships (direct, quasi-direct and indirect). In order to be able to effectively process gathered data using data science approaches we need to develop new methodology that will focus on the multirelational (more than one type of connections in a network) character of data.
In general, there are two methods to do that: (i) analyse each relation type separately and then combine results from different layers or (ii) merge all relation types in one layer and analyse this newly created layer. Both approaches require effort in terms of redefining existing network analysis techniques. Analysing each network separately means that methods for combining results from different layers need to be developed. Merging some/all connection types into one heterogeneous relation means that a new approach for aggregation of data from different layers is required. Only by developing rigid approaches to data aggregation, the analytics task can be performed.
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