Schweimer Christoph, Gfrerer Christine, Lugstein Florian, Pape David, Velimsky Jan, Elsässer Robert, Geiger Bernhard
2022
Online social networks are a dominant medium in everyday life to stay in contact with friends and to share information. In Twitter, users can connect with other users by following them, who in turn can follow back. In recent years, researchers studied several properties of social networks and designed random graph models to describe them. Many of these approaches either focus on the generation of undirected graphs or on the creation of directed graphs without modeling the dependencies between reciprocal (i.e., two directed edges of opposite direction between two nodes) and directed edges. We propose an approach to generate directed social network graphs that creates reciprocal and directed edges and considers the correlation between the respective degree sequences.Our model relies on crawled directed graphs in Twitter, on which information w.r.t.\ a topic is exchanged or disseminated. While these graphs exhibit a high clustering coefficient and small average distances between random node pairs (which is typical in real-world networks), their degree sequences seem to follow a $\chi^2$-distribution rather than power law. To achieve high clustering coefficients, we apply an edge rewiring procedure that preserves the node degrees.We compare the crawled and the created graphs, and simulate certain algorithms for information dissemination and epidemic spreading on them. The results show that the created graphs exhibit very similar topological and algorithmic properties as the real-world graphs, providing evidence that they can be used as surrogates in social network analysis. Furthermore, our model is highly scalable, which enables us to create graphs of arbitrary size with almost the same properties as the corresponding real-world networks.
Schweimer Christoph, Geiger Bernhard, Wang Meizhu, Gogolenko Sergiy, Gogolenko Sergiy, Mahmood Imran, Jahani Alireza, Suleimenova Diana, Groen Derek
2021
Automated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of interest and edges to direct travelling routes between them. Our approach involves two steps. In the first step, we use a routing service to compute distances between all pairs of L locations, resulting in a complete graph. In the second step, we prune this graph by removing edges corresponding to indirect routes, identified using the triangle inequality. The computational complexity of this second step is O(L3), which enables the computation of location graphs for all towns and cities on the road network of an entire continent. To illustrate the utility of our algorithm in an application, we constructed location graphs for four regions of different size and road infrastructures and compared them to manually created ground truths. Our algorithm simultaneously achieved precision and recall values around 0.9 for a wide range of the single hyperparameter, suggesting that it is a valid approach to create large location graphs for which a manual creation is infeasible.
Schweimer Christoph, Geiger Bernhard, Wang Meizhu, Gogolenko Sergiy, Mahmood Imran, Jahani Alireza, Suleimenova Diana, Groen Derek
2021
Velimsky Jan, Schweimer Christoph, Tran Thi Ngoc Han, Gfrerer Christine
2020
In this paper, we investigate the information sharing patterns via Twitter for the social media networks of two ideologically divergent political parties, the Freedom Party (FPOE) and the NEOS, in the lead-up to and during the 2019 Austrian National Council Elections and ask: 1) To what extent do the associated networks differ in their structure?2) Which determinants affect the spreading behaviour of messages in the two networks, and which factors explain these differences? 3) What type of political news and information did verified users (e.g., news media or politicians) share ahead of the vote and which role do these users play in the dissemination of messages in the respective networks. Analysing approximately 200,000 tweets, the study relies on qualitative and quantitative text analysis including sentiment analysis, on supervised classification of relevant attributes for the message spread combined with neural network models retrieving the retweet probabilities for source tweets and on network analysis. In addition to notable differences between the two parties in network structure and Twitter usage, we find that verified users, as well as URLs, other media elements (videos or photos) and hashtags play an important role in the spreading of messages. We also reveal that negative sentiments have a higher retweetability compared to other sentiments. Interestingly, gender seems to matter in the network related to the FPOE, where male users get more retweets than female users.
Schweimer Christoph, Geiger Bernhard, Suleimenova Diana, Groen Derek, Gfrerer Christine, Pape David, Elsaesser Robert, Kocsis Albert Tihamér, Liszkai B., Horváth Zoltan
2019