Publications
Proceedings, refereed
A Streaming System for Large-scale Temporal Graph Mining of Reddit Data
In 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). Lyon, France: IEEE, 2022.Status: Published
A Streaming System for Large-scale Temporal Graph Mining of Reddit Data
| Afilliation | Scientific Computing, Machine Learning |
| Project(s) | Department of High Performance Computing , Enabling Graph Neural Networks at Exascale |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) |
| Pagination | 1153-1162 |
| Publisher | IEEE |
| Place Published | Lyon, France |
| URL | https://ieeexplore.ieee.org/document/9835250/http://xplorestaging.ieee.o... |
| DOI | 10.1109/IPDPSW55747.2022.00189 |
Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks
In 20th International Symposium on Experimental Algorithms (SEA 2022). Vol. 233. Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2022.Status: Published
Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks
| Afilliation | Scientific Computing |
| Project(s) | Department of High Performance Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | 20th International Symposium on Experimental Algorithms (SEA 2022) |
| Volume | 233 |
| Pagination | 12:1–12:17 |
| Publisher | Schloss Dagstuhl – Leibniz-Zentrum für Informatik |
| Place Published | Dagstuhl, Germany |
| ISBN Number | 978-3-95977-251-8 |
| ISSN Number | 1868-8969 |
| URL | https://drops.dagstuhl.de/opus/volltexte/2022/16546 |
| DOI | 10.4230/LIPIcs.SEA.2022.12 |
Implementing Spatio-Temporal Graph Convolutional Networks on Graphcore IPUs
In 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). Lyon, France: IEEE, 2022.Status: Published
Implementing Spatio-Temporal Graph Convolutional Networks on Graphcore IPUs
| Afilliation | Machine Learning |
| Project(s) | Department of High Performance Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) |
| Pagination | 45-54 |
| Publisher | IEEE |
| Place Published | Lyon, France |
| URL | https://ieeexplore.ieee.org/document/9835385/http://xplorestaging.ieee.o... |
| DOI | 10.1109/IPDPSW55747.2022.00016 |
Journal Article
COVID-19 and 5G conspiracy theories: Long term observation of a digital wildfire
International Journal of Data Science and Analytics (2022).Status: Published
COVID-19 and 5G conspiracy theories: Long term observation of a digital wildfire
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | International Journal of Data Science and Analytics |
| Publisher | Springer |
The connectivity network underlying the German’s Twittersphere: a testbed for investigating information spreading phenomena
Scientific Reports 12, no. 1 (2022).Status: Published
The connectivity network underlying the German’s Twittersphere: a testbed for investigating information spreading phenomena
Online social networks are ubiquitous, have billions of users, and produce large amounts of data. While platforms like Reddit are based on a forum-like organization where users gather around topics, Facebook and Twitter implement a concept in which individuals represent the primary entity of interest. This makes them natural testbeds for exploring individual behavior in large social networks. Underlying these individual-based platforms is a network whose “friend” or “follower” edges are of binary nature only and therefore do not necessarily reflect the level of acquaintance between pairs of users. In this paper,we present the network of acquaintance “strengths” underlying the German Twittersphere. To that end, we make use of the full non-verbal information contained in tweet–retweet actions to uncover the graph of social acquaintances among users, beyond pure binary edges. The social connectivity between pairs of users is weighted by keeping track of the frequency of shared content and the time elapsed between publication and sharing. Moreover, we also present a preliminary topological analysis of the German Twitter network. Finally, making the data describing the weighted German Twitter network of acquaintances, we discuss how to apply this framework as a ground basis for investigating spreading phenomena of particular contents.
| Afilliation | Communication Systems |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires, Enabling Graph Neural Networks at Exascale |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Scientific Reports |
| Volume | 12 |
| Issue | 1 |
| Date Published | Jan-12-2022 |
| Publisher | Nature Publishing Group |
| URL | https://www.nature.com/articles/s41598-022-07961-3 |
| DOI | 10.1038/s41598-022-07961-3 |
Dr. Daniel Thilo Schroeder, residing in Oslo, is a Research Scientist and Associate Professor specializing in big data analytics, complex networks, and digital communication. He is a member of the Smart Data group at SINTEF, contributing to the development of sustainable platforms for processing extreme data and building high-quality, FAIR-compliant datasets that fortify the effectiveness of AI applications. In his position as Associate Professor at Oslo Metropolitan University, Dr. Schroeder explores the influence of digital communication on conflict development and mediation in sub-Saharan Africa. During his postdoc at the Simula Research Laboratory, he engaged in pivotal work to expand the application of deep learning to unstructured data through the development of computational frameworks. Earning his Ph.D. from the Technical University of Berlin, Dr. Schroeder has been involved in multiple projects centered around understanding and mitigating the rapid spread of online misinformation.
PhD Thesis
Explaining News Spreading Phenomena in Social Networks
In Technische Universität Berlin. Vol. PhD, 2022.Status: Published
Explaining News Spreading Phenomena in Social Networks
| Afilliation | Machine Learning |
| Project(s) | Department of High Performance Computing |
| Publication Type | PhD Thesis |
| Year of Publication | 2022 |
| Degree awarding institution | Technische Universität Berlin |
| Degree | PhD |
Journal Article
Don't Trust Your Eyes: Image Manipulation in the Age of DeepFakes
Frontiers in Communication 6 (2021).Status: Published
Don't Trust Your Eyes: Image Manipulation in the Age of DeepFakes
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Frontiers in Communication |
| Volume | 6 |
| Publisher | Frontiers Media SA |
| Place Published | Lausanne, Switzerland |
| URL | https://www.frontiersin.org/articles/10.3389/fcomm.2021.632317/full |
| DOI | 10.3389/fcomm.2021.632317 |
Talk, keynote
Explaining News Spreading Phenomena in Social Networks
In Händlerlogo BI Norwegian Business School, 2021.Status: Published
Explaining News Spreading Phenomena in Social Networks
Digital wildfires are fast spreading online misinformation phenomena with the potential to cause harm in the physical world. They have been identified as a considerable risk to developed societies which raised the need to better understand online misinformation phenomena to mitigate that risk. We approach the problem from an interdisciplinary angle with the aim of using large scale analysis of social network data to test hypotheses about the behavior of social network users interacting with misinformation. We discuss state of the art techniques for capturing large volumes of communication data from social networks such as Twitter as well as collections of news such as GDELT. Based on that we describe new methods on how the reach as well as the typical target audience of media and social network participants can be measured. Doing so allows the testing of hypotheses such as the existence of filter bubbles through the use of large amounts of real-world data. Finally we discuss how the detection of anomalies in the typical news spreading patterns can be used to detect disinformation campaigns and digital wildfires.
| Afilliation | Communication Systems |
| Project(s) | Enabling Graph Neural Networks at Exascale, UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Talk, keynote |
| Year of Publication | 2021 |
| Location of Talk | Händlerlogo BI Norwegian Business School |
Proceedings, refereed
iPUG: Accelerating Breadth-First Graph Traversals Using Manycore Graphcore IPUs
In High Performance Computing. ISC High Performance 2021. Vol. LNCS, volume 12728. Cham: Springer International Publishing, 2021.Status: Published
iPUG: Accelerating Breadth-First Graph Traversals Using Manycore Graphcore IPUs
The Graphcore Intelligence Processing Unit (IPU) is a newly developed processor type whose architecture does not rely on the traditional caching hierarchies. Developed to meet the need for more and more data-centric applications, such as machine learning, IPUs combine a dedicated portion of SRAM with each of its numerous cores, resulting in high memory bandwidth at the price of capacity. The proximity of processor cores and memory makes the IPU a promising field of experimentation for graph algorithms since it is the unpredictable, irregular memory accesses that lead to performance losses in traditional processors with pre-caching.
This paper aims to test the IPU’s suitability for algorithms with hard-to-predict memory accesses by implementing a breadth-first search (BFS) that complies with the Graph500 specifications. Precisely because of its apparent simplicity, BFS is an established benchmark that is not only subroutine for a variety of more complex graph algorithms, but also allows comparability across a wide range of architectures.
We benchmark our IPU code on a wide range of instances and compare its performance to state-of-the-art CPU and GPU codes. The results indicate that the IPU delivers speedups of up to 4×4× over the fastest competing result on an NVIDIA V100 GPU, with typical speedups of about 1.5×1.5× on most test instances.
| Afilliation | Scientific Computing |
| Project(s) | Department of High Performance Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | High Performance Computing. ISC High Performance 2021 |
| Volume | LNCS, volume 12728 |
| Pagination | 291-309 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-030-78712-7 |
| ISSN Number | 0302-9743 |
| Keywords | BFS, Graph500, IPU, Performance optimization |
| URL | https://link.springer.com/10.1007/978-3-030-78713-4 |
| DOI | 10.1007/978-3-030-78713-4 |
WICO Graph: a Labeled Dataset of Twitter Subgraphs based on Conspiracy Theory and 5G-Corona Misinformation Tweets
In Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). Vol. 2. SCITEPRESS, 2021.Status: Published
WICO Graph: a Labeled Dataset of Twitter Subgraphs based on Conspiracy Theory and 5G-Corona Misinformation Tweets
In the wake of the COVID-19 pandemic, a surge of misinformation has flooded social media and other internet channels, and some of it has the potential to cause real-world harm. To counteract this misinformation, reliably identifying it is a principal problem to be solved. However, the identification of misinformation poses a formidable challenge for language processing systems since the texts containing misinformation are short, work with insinuation rather than explicitly stating a false claim, or resemble other postings that deal with the same topic ironically. Accordingly, for the development of better detection systems, it is not only essential to use hand-labeled ground truth data and extend the analysis with methods beyond Natural Language Processing to consider the characteristics of the participant's relationships and the diffusion of misinformation. This paper presents a novel dataset that deals with a specific piece of misinformation: the idea that the 5G wireless network is causally connected to the COVID-19 pandemic. We have extracted the subgraphs of 3,000 manually classified Tweets from Twitter's follower network and distinguished them into three categories. First, subgraphs of Tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and Tweets that do neither. We created the WICO (Wireless Networks and Coronavirus Conspiracy) dataset to support experts in machine learning experts, graph processing, and related fields in studying the spread of misinformation. Furthermore, we provide a series of baseline experiments using both Graph Neural Networks and other established classifiers that use simple graph metrics as features. The dataset is available at https://datasets.simula.no/wico-graph
| Afilliation | Scientific Computing |
| Project(s) | Department of High Performance Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021) |
| Volume | 2 |
| Pagination | 257-266 |
| Publisher | SCITEPRESS |
| ISBN Number | 978-989-758-484-8 |
| DOI | 10.5220/0010262802570266 |
WICO Text: A Labeled Dataset of Conspiracy Theory and 5G-Corona Misinformation Tweets
In Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks (OASIS '21). ACM, 2021.Status: Published
WICO Text: A Labeled Dataset of Conspiracy Theory and 5G-Corona Misinformation Tweets
| Afilliation | Machine Learning |
| Project(s) | Department of High Performance Computing , Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks (OASIS '21) |
| Pagination | 21-25 |
| Publisher | ACM |
Talks, invited
Spreading Online Misinformation
In Data-SKUP 2021, 2021.Status: Published
Spreading Online Misinformation
Digital wildfires are fast spreading online misinformation phenomena with the potential to cause harm in the physical world. They have been identified as a considerable risk to developed societies which raised the need to better understand online misinformation phenomena to mitigate that risk. We approach the problem from an interdisciplinary angle with the aim of using large scale analysis of social network data to test hypotheses about the behavior of social network users interacting with misinformation. We discuss state of the art techniques for capturing large volumes of communication data from social networks such as Twitter as well as collections of news such as GDELT. Based on that we describe new methods on how the reach as well as the typical target audience of media and social network participants can be measured. Doing so allows the testing of hypotheses such as the existence of filter bubbles through the use of large amounts of real-world data. Finally we discuss how the detection of anomalies in the typical news spreading patterns can be used to detect disinformation campaigns and digital wildfires.
| Afilliation | Scientific Computing |
| Project(s) | Department of High Performance Computing , Simula Metropolitan Center for Digital Engineering |
| Publication Type | Talks, invited |
| Year of Publication | 2021 |
| Location of Talk | Data-SKUP 2021 |
Talks, contributed
Will technical means help in preventing digital wildfires?
In 6th World Conference on Media and Mass Communication, Cagliari, Italy, 2021.Status: Published
Will technical means help in preventing digital wildfires?
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Talks, contributed |
| Year of Publication | 2021 |
| Location of Talk | 6th World Conference on Media and Mass Communication, Cagliari, Italy |
Proceedings, refereed
A Framework for Interaction-based Propagation Analysis in Online Social Networks
In Complex Networks. Springer, 2020.Status: Accepted
A Framework for Interaction-based Propagation Analysis in Online Social Networks
Online social networks create a digital footprint of human interaction naturally by the way they function. Thus, they allow a large-scale analysis of human behavior which was previously infeasible for social scientists. Consequently, social networks have been studied intensely in the last decade. The core of most social networks is the relationship between users which can be described as a graph. The graph can be either undirected, as is the case for the friendship relation of Facebook, or directed, which is the case of the follower relation on Twitter. The relationship is readily visible, e.g. on the user interface of the social networks themselves. However, these edges are unweighted expressions of interest and reflect how individuals have chosen to relate to each other rather than how they actually interact with each other. For studying information propagation, comparing interaction properties is crucial and, therefore, using models based on connections that reflect different dimensions and strengths of acquaintance seems appropriate. Thus, there is a need for obtaining weighted edges from the communication that occurs on the social network. In this paper, we present a novel method to calculate an acquaintance score between pairs of Twitter users and use the resulting networks to enable the analysis of interaction based information propagation. By understanding the frequency and velocity with which individuals share content as a measure of acquaintance, it becomes possible to predict, compare communication patterns, and detect unusual communication. In contrast to previous work which assigns edge weights based on tie strength, our score considers the response time as a crucial factor and, therefore, enables time-based spreading comparisons.
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires, Department of High Performance Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | Complex Networks |
| Publisher | Springer |
| Notes | Extended abstract |
A Scalable System for Bundling Online Social Network Mining Research
In 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2020.Status: Published
A Scalable System for Bundling Online Social Network Mining Research
Online social networks such as Facebook and Twitter are part of the everyday life of millions of people. They are not only used for interaction but play an essential role when it comes to information acquisition and knowledge gain. The abundance and detail of the accumulated data in these online social networks open up new possibilities for social researchers and psychologists, allowing them to study behavior in a large test population. However, complex application programming interfaces (API) and data scraping restrictions are, in many cases, a limiting factor when accessing this data. Furthermore, research projects are typically granted restricted access based on quotas. Thus, research tools such as scrapers that access social network data through an API must manage these quotas. While this is generally feasible, it becomes a problem when more than one tool, or multiple instances of the same tool, is being used in the same research group. Since different tools typically cannot balance access to a shared quota on their own, additional software is needed to prevent the individual tools from overusing the shared quota. In this paper, we present a proxy server that manages several researchers' data contingents in a cooperative research environment and thus enables a transparent view of a subset of Twitter's API. Our proxy scales linearly with the number of clients in use and incurs almost no performance penalties or implementation overhead to further layer or applications that need to work with the Twitter API. Thus, it allows seamless integration of multiple API accessing programs within the same research group.
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires, Department of High Performance Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS) |
| Pagination | 1-6 |
| Publisher | IEEE |
A System for High Performance Mining on GDELT Data
In 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2020.Status: Published
A System for High Performance Mining on GDELT Data
We design a system for efficient in-memory analysis of data from the GDELT database of news events. The specialization of the system allows us to avoid the inefficiencies of existing alternatives, and make full use of modern parallel high-performance computing hardware. We then present a series of experiments showcasing the system’s ability to analyze correlations in the entire GDELT 2.0 database containing more than a billion news items. The results reveal large scale trends in the world of today’s online news.
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) |
| Date Published | 05/2020 |
| Publisher | IEEE |
| Keywords | Data mining, GDELT, High Performance Computing, Misinformation, Publishing |
Evaluating Standard Classifiers for Detecting COVID-19 related Misinformation
In MediaEval 2020. CEUR, 2020.Status: Published
Evaluating Standard Classifiers for Detecting COVID-19 related Misinformation
| Afilliation | Machine Learning |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires, Department of High Performance Computing , Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | MediaEval 2020 |
| Publisher | CEUR |
FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020
In Media Eval Challange 2020. CEUR, 2020.Status: Published
FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020
| Afilliation | Scientific Computing, Machine Learning |
| Project(s) | Department of High Performance Computing , Department of Holistic Systems, UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | Media Eval Challange 2020 |
| Publisher | CEUR |
Resource Efficient Algorithms for Message Sampling in Online Social Networks
In The Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS-2020). IEEE, 2020.Status: Published
Resource Efficient Algorithms for Message Sampling in Online Social Networks
Sampling the network structure of online social networks is a widely discussed topic as it enables a wide variety of research in computational social science and associated fields. However, analyzing and sampling contentful messages still lacks effective solutions. Previous work for retrieving messages from social networks either used endpoints that are not available to the general research community or analyzed a predefined stream of messages. Our work uses features of the Twitter API that we utilize to construct a data structure that optimizes the efficiency of requests sent to the social network. Moreover, we present a strategy for selecting users to sample, which improves the effectiveness of our query optimizing data structure by leveraging existing models of user behavior. Combining our data structure with our proposed algorithm, we can achieve a 92% sampling efficiency over long timeframes.
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires, Department of High Performance Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | The Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS-2020) |
| Publisher | IEEE |
Poster
A Framework for Interaction-based Propagation Analysis in Online Social Networks
Complex Networks, 2020.Status: Published
A Framework for Interaction-based Propagation Analysis in Online Social Networks
| Afilliation | Scientific Computing |
| Project(s) | Department of High Performance Computing , Department of Holistic Systems, UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Poster |
| Year of Publication | 2020 |
| Place Published | Complex Networks |
Graph Structure Based Monitoring of Digital Wildfires
6th International Conference on Computational Social Science, Boston, MA, USA, 2020.Status: Published
Graph Structure Based Monitoring of Digital Wildfires
| Afilliation | Scientific Computing |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires |
| Publication Type | Poster |
| Year of Publication | 2020 |
| Date Published | 07/2020 |
| Place Published | 6th International Conference on Computational Social Science, Boston, MA, USA |
| Type of Work | Poster |
| Keywords | Digital wildfires, Graph neural networks, Large scale infrastructure, Misinformation, Social network analysis |
| URL | http://2020.ic2s2.org/program |
Poster
EXA - A distributed computation environment
Geilo Wintrerschool 2019, 2019.Status: Published
EXA - A distributed computation environment
| Afilliation | Scientific Computing |
| Project(s) | Department of High Performance Computing , Department of Holistic Systems |
| Publication Type | Poster |
| Year of Publication | 2019 |
| Place Published | Geilo Wintrerschool 2019 |
Proceedings, refereed
FACT: a Framework for Analysis and Capture of Twitter Graphs
In The Sixth IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2019) . IEEE, 2019.Status: Published
FACT: a Framework for Analysis and Capture of Twitter Graphs
In the recent years, online social networks have become an important source of news and the primary place for political debates for a growing part of the population. At the same time, the spread of fake news and digital wildfires (fast- spreading and harmful misinformation) has become a growing concern worldwide, and online social networks the problem is most prevalent. Thus, the study of social networks is an essential component in the understanding of the fake news phenomenon. Of particular interest is the network connectivity between participants, since it makes communication patterns visible. These patterns are hidden in the offline world, but they have a profound impact on the spread of ideas, opinions and news. Among the major social networks, Twitter is of special interest. Because of its public nature, Twitter offers the possibility to perform research without the risk of breaching the expectation of privacy. However, obtaining sufficient amounts of data from Twitter is a fundamental challenge for many researchers. Thus, in this paper, we present a scalable framework for gathering the graph structure of follower networks, posts and profiles. We also show how to use the collected data for high-performance social network analysis.
| Afilliation | Software Engineering |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires, Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | The Sixth IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2019) |
| Pagination | 134-141 |
| Publisher | IEEE |
| DOI | 10.1109/SNAMS.2019.8931870 |
Dr. Daniel Thilo Schroeder, residing in Oslo, is a Research Scientist and Associate Professor specializing in big data analytics, complex networks, and digital communication. He is a member of the Smart Data group at SINTEF, contributing to the development of sustainable platforms for processing extreme data and building high-quality, FAIR-compliant datasets that fortify the effectiveness of AI applications. In his position as Associate Professor at Oslo Metropolitan University, Dr. Schroeder explores the influence of digital communication on conflict development and mediation in sub-Saharan Africa. During his postdoc at the Simula Research Laboratory, he engaged in pivotal work to expand the application of deep learning to unstructured data through the development of computational frameworks. Earning his Ph.D. from the Technical University of Berlin, Dr. Schroeder has been involved in multiple projects centered around understanding and mitigating the rapid spread of online misinformation.
Graph-based Feature Selection Filter Utilizing Maximal Cliques
In The Sixth IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2019) . IEEE, 2019.Status: Published
Graph-based Feature Selection Filter Utilizing Maximal Cliques
Huge amounts of data are collected every millisecond all around the world. This ranges from images and videos to an increasing amount of sensor data. Thus, it gets difficult for humans to decide on the most important features anymore. But reducing the feature vector is an important and necessary task to achieve higher precision in classification tasks. Detecting anomalies and classifying data points is crucial for a variety of objectives in many domains. Therefore, this work focuses on feature selection for binary decision problems (e.g. anomaly detection, binary classification). We propose a novel graph-based feature selection filter, which takes into account both the importance and correlation of features at the same time. The graph-based feature selection filter recommends a subset by applying a rating function onto the maximal cliques of the graph. The evaluation is based on a comparison of the accuracy of multiple machine learning algorithms and datasets between different baseline feature selection approaches and the proposed approach. Results show that the proposed approach delivers the highest accuracy in about 69% of the cases compared to existing approaches, while reducing the number of features.
| Afilliation | Machine Learning |
| Project(s) | No Simula project, Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | The Sixth IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2019) |
| Pagination | 297-302 |
| Publisher | IEEE |
Dr. Daniel Thilo Schroeder, residing in Oslo, is a Research Scientist and Associate Professor specializing in big data analytics, complex networks, and digital communication. He is a member of the Smart Data group at SINTEF, contributing to the development of sustainable platforms for processing extreme data and building high-quality, FAIR-compliant datasets that fortify the effectiveness of AI applications. In his position as Associate Professor at Oslo Metropolitan University, Dr. Schroeder explores the influence of digital communication on conflict development and mediation in sub-Saharan Africa. During his postdoc at the Simula Research Laboratory, he engaged in pivotal work to expand the application of deep learning to unstructured data through the development of computational frameworks. Earning his Ph.D. from the Technical University of Berlin, Dr. Schroeder has been involved in multiple projects centered around understanding and mitigating the rapid spread of online misinformation.