TL;DR
This article systematically introduces and analyzes the various concepts of temporal network motifs and their corresponding discovery algorithms and lists the challenges and opportunities in temporal network motif research.
Network motifs, recurrent local patterns of interactions in graphs, provide fundamental insights on the interplay between structure and functionality in complex systems. Many real-world systems are not well represented by traditional static pairwise networks, as interactions may involve groups of nodes, occur over time...
Identifying influential spreaders in temporal networks is essential for understanding and controlling information and disease propagation. Among existing centrality methods, local methods have gained considerable attention due to their computational efficiency and their ability to identify influential spreaders usi...
Many real systems can be represented as growing networks where new nodes and links gradually emerge. The Barab\'asi-Albert model for growing networks, and many models inspired by it, are based on the idea that nodes compete for links. However, the strength and the very presence of this competition have not been tested....
The MPCount implementation builds on the FaSE algorithm, extending it to accommodate multiplex networks by adapting its efficient enumeration and isomorphism identification process to address the introduced layers, making it an available practical tool for counting subgraphs in multiplex networks.
Integrative analysis of the properties of multiple networks pertaining to a biological system is a key problem in systems biology. But computing the properties of thousands of large networks poses a challenge. Most techniques that partially address this challenge, such as parallelism and optimized algo-rithms, treat th...
Understanding the generative mechanism of real-world networks is crucial for analyzing connection patterns and making inference from network data. Graphons are widely used to model such mechanisms. Network histogram methods are nonparametric approaches based on blockmodel approximations that provide an intuitive view o...
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