Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Blöcker, Christopher"'
Predicting future interactions or novel links in networks is an indispensable tool across diverse domains, including genetic research, online social networks, and recommendation systems. Among the numerous techniques developed for link prediction, th
Externí odkaz:
http://arxiv.org/abs/2410.08777
Graph pooling is an essential part of deep graph representation learning. We introduce MapEqPool, a principled pooling operator that takes the inherent hierarchical structure of real-world graphs into account. MapEqPool builds on the map equation, an
Externí odkaz:
http://arxiv.org/abs/2409.10263
Dynamic link prediction is an important problem considered by many recent works proposing various approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on publicly available benchmark datasets involving contin
Externí odkaz:
http://arxiv.org/abs/2406.04897
Autor:
Smiljanić, Jelena, Blöcker, Christopher, Holmgren, Anton, Edler, Daniel, Neuman, Magnus, Rosvall, Martin
Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. Howev
Externí odkaz:
http://arxiv.org/abs/2311.04036
Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on objective functions, optimised
Externí odkaz:
http://arxiv.org/abs/2310.01144
Researchers use networks to model relational data from complex systems, and tools from network science to map them and understand their function. Flow communities capture the organization of various real-world systems such as social networks, protein
Externí odkaz:
http://arxiv.org/abs/2304.05775
Publikováno v:
Proceedings of the First Learning on Graphs Conference, PMLR 198:52:1-52:18, 2022
Node similarity scores are a foundation for machine learning in graphs for clustering, node classification, anomaly detection, and link prediction with applications in biological systems, information networks, and recommender systems. Recent works on
Externí odkaz:
http://arxiv.org/abs/2208.14220
Publikováno v:
Appl Netw Sci 7, 56 (2022)
To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centra
Externí odkaz:
http://arxiv.org/abs/2201.12590
Publikováno v:
J. Complex Netw. 9, cnab044 (2021)
Detecting significant community structure in networks with incomplete observations is challenging because the evidence for specific solutions fades away with missing data. For example, recent research shows that flow-based community detection methods
Externí odkaz:
http://arxiv.org/abs/2106.14798
Autor:
Blöcker, Christopher, Rosvall, Martin
Publikováno v:
Phys. Rev. E 102, 052305 (2020)
Mapping network flows provides insight into the organization of networks, but even though many real-networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how
Externí odkaz:
http://arxiv.org/abs/2007.01666