Network seminar "Higher-Order Networks and Motif Analysis in Hypergraphs": 5th October
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Liubov
Sep 27, 2023, 7:01:41 AM9/27/23
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Dear all,
We are happy to start the network seminar series this year with the talk on "Higher-Order Networks and Motif Analysis in Hypergraphs" on the 5th October at 4pm CET by Quintino Francesco Lotito.
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Title: Higher-Order Networks and Motif Analysis in Hypergraphs
Abstract: Over the last two decades, networks have emerged as a powerful tool to analyze the complex topology of interacting systems. From social networks to the brain, several systems have been represented as a collection of nodes and links, encoding dyadic interactions among pairs of units. Yet, growing empirical evidence is now suggesting that a large number of such interactions are not limited to pairs, but rather occur in larger groups. In this seminar, we will discuss how more sophisticated mathematical frameworks such as the hypergraphs can enhance our modeling capabilities for systems involving higher-order interactions. We will see that dealing with such complex structures requires new algorithms to cope with more computationally difficult problems, and new tools and generalizations of classic network ideas to fully exploit the improvements in the expressive power. In the last part of the talk, we will focus on the specific problem of higher-order motif analysis. Higher-order network motifs are defined as statistically over-expressed connected subgraphs of a given number of nodes, which can be connected by higher-order interactions of arbitrary order. We will show how they are able to characterize the local structure of hypergraphs and extract fingerprints at the network microscale of higher-order real-world systems. Moreover, we will discuss the problem from an algorithmic perspective, investigating also some real-world applications. Finally, we will talk about open challenges and possible future directions.
Abstract: Over the last two decades, networks have emerged as a powerful tool to analyze the complex topology of interacting systems. From social networks to the brain, several systems have been represented as a collection of nodes and links, encoding dyadic interactions among pairs of units. Yet, growing empirical evidence is now suggesting that a large number of such interactions are not limited to pairs, but rather occur in larger groups. In this seminar, we will discuss how more sophisticated mathematical frameworks such as the hypergraphs can enhance our modeling capabilities for systems involving higher-order interactions. We will see that dealing with such complex structures requires new algorithms to cope with more computationally difficult problems, and new tools and generalizations of classic network ideas to fully exploit the improvements in the expressive power. In the last part of the talk, we will focus on the specific problem of higher-order motif analysis. Higher-order network motifs are defined as statistically over-expressed connected subgraphs of a given number of nodes, which can be connected by higher-order interactions of arbitrary order. We will show how they are able to characterize the local structure of hypergraphs and extract fingerprints at the network microscale of higher-order real-world systems. Moreover, we will discuss the problem from an algorithmic perspective, investigating also some real-world applications. Finally, we will talk about open challenges and possible future directions.
Bio: Quintino Francesco Lotito is a PhD student in Computer Science at the University of Trento in Italy. His research focuses on the development of statistical methods and efficient algorithms to analyze network data. In particular, he is interested in characterizing the structural organization at multiple scales of real-world complex systems with group interactions, from social to biological systems.
https://arxiv.org/pdf/2303.15356.pdf python library for higher-order network analysis
https://www.nature.com/articles/s42005-022-00858-7 higher-order network motif analysis in hypergraphs https://www.sciencedirect.com/science/article/pii/S0370157320302489 big review about higher-order networks
Bests,
Liubov
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Phd, Humboldt University of Berlin
Researcher, Bell labs France
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